Each month, Lumar’s in-house tech SEO experts hand-pick some of the SEO industry’s top news stories from across the web to keep you up-to-date on all things SEO and website optimization.

For our May 2026 SEO & GEO/AEO news roundup, the top headlines include:  

• Google I/O 2026 brought the biggest Search overhaul in 25 years — Plus: AI Mode has surpassed one billion monthly users just one year after launch, with query volume hitting an all-time high last quarter.
• Google published its first official AI search optimization guide covering crawlability, content structure, and technical SEO foundations for AI Mode and AI Overviews.
• Google’s May 2026 core update began rolling out on May 21st — it’s the second core update of the year, running in parallel with ongoing AI feature expansion in Search.
• Google launched a cross-channel shopping cart experience across Search, Gemini, and YouTube — letting users save and manage products across platforms as part of a more agentic shopping journey.
• Google Analytics added a new AI assistant traffic channel in GA4 — automatically grouping visits from tools like ChatGPT, Gemini, and Claude under a dedicated ai-assistant medium.
• AI search is more likely to cite in-depth content — that’s according to Google’s Nick Fox at Google Marketing Live 2026.
• Google removed the last remaining FAQ rich results from Search — including the phasing out of FAQ reporting in Search Console, marking another step away from traditional rich result features.
• Snap and Perplexity ended their planned $400 million AI search integration deal — a reminder that AI search distribution partnerships inside major consumer platforms are far from guaranteed.
• OpenAI made GPT-5.5 Instant ChatGPT’s new default model — with improvements in factuality, image understanding, and the model’s ability to decide when to use web search to inform its generative responses.
• (and more, below!)

Dive into the details behind the SEO and GEO/AEO headlines this month below…

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May 2026 SEO & GEO/AEO News Roundup:

Google I/O 2026: Google unveils biggest Search overhaul in 25 years as AI Mode hits 1 billion monthly users 

The hotly anticipated Google I/O 2026 summit saw Google unveil what it calls the biggest transformation in Search for more than 25 years. Updates include a redesigned AI-first search experience that adds more AI features than ever before, from persistent conversational context to autonomous “information agents” designed to help complete tasks.

At the I/O event, Google announced that, just one year after its debut, AI Mode has surpassed one billion monthly users, with queries more than doubling every quarter since launch. Google also reported that query volume reached an all-time high last quarter — framing AI Mode not as a replacement for Search but as a driver of more searching, not less.

The Google Search updates announced at I/O 2026 stop short of making AI Mode the default for Google Search, but it does reinforce how far we’ve moved away from traditional link-based SERPs. Search is becoming increasingly reliant on these task-oriented AI experiences, the likes of which promise to make tracking and monitoring SEO performance a whole lot more complicated.

(Source: Google’s The Keyword Blog ; Google I/O Keynote Videos)

Google publishes AI search optimization guide

Google has published its first formal documentation on optimizing for AI, “Optimizing your website for generative AI features on Google Search.” The focus is naturally on Google-owned features such as AI Mode and AI Overviews; however, in theory much of it should be transferable to other AI systems.

Much like traditional search, the emphasis is put on creating helpful, structured, and easily accessible content. The document also highlights the importance of page clarity and crawlability, with strong technical SEO foundations continuing to prove significant.

(Source: Google Developers Blog)

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Google introduces cross-channel “universal” shopping carts

Google has announced a new shopping cart experience that works across platforms such as Search, Gemini and YouTube. For the first time, users can save, track, and manage products in one place while navigating across different apps and products.

The feature is designed to support more agentic shopping journeys, with AI surfacing price changes, availability updates, and product recommendations. It reinforces Google’s shift towards an ongoing AI-assisted shopping experience, rather than a series of isolated searches.

Per Google’s Universal Cart announcement:

“Universal Cart is an intelligent shopping cart and your new hub for shopping on Google. It works across merchants and across services, so you can add things to your cart while you’re browsing Search, chatting with Gemini, watching YouTube or even reading your Gmail.”

“The moment you add a product to your cart, it gets to work in the background — finding deals and price drops, giving you insights on price history and alerting you when an item is back in stock. It all runs on our Gemini models, so your cart gets even smarter as the models improve.”

(Source: Google’s The Keyword Blog)

Google releases data on how people are using AI Mode

A year on from the full launch of AI Mode in the US, Google has published data on its usage. The report shows that users are increasingly treating AI Mode differently to traditional search, leaning heavily on its ability to process complex, multi-layered queries.

Searches in AI Mode tend to be more exploratory in nature, with users asking follow-up questions and sometimes engaging in lengthy conversations with the bot. For site owners, it’s less about ranking for specific queries and more about ensuring your brand is visible in AI-driven systems.

Per the Google publication, “How people are using AI Mode in the US”: 

“AI Mode isn’t just changing how people search — it’s expanding the very definition of what’s searchable.”

(Source: Google)

Google launches its May 2026 core update

Google officially announced the rollout of the May 2026 core update on May 21, 2026. Google described it as “a regular update designed to better surface relevant, satisfying content for searchers from all types of sites,” and the rollout is expected to take approximately two weeks to complete. The update is global, affecting all regions and languages.

The May core update is Google’s second of 2026. As usual, no specific focus has been publicly stated for this core update, but its timing is interesting given the ongoing volatility caused by the expansion of AI features in Search. Both occurring in parallel will make it harder to separate algorithmic ranking shifts from broader AI-driven traffic changes.

(Source: Search Engine Journal)

Google Analytics adds new AI assistant traffic channel

Google has introduced a new AI assistant traffic category in GA4 that automatically groups visits coming from recognized AI tools such as ChatGPT, Gemini, and Claude.

The update assigns a new ai-assistant medium, putting this traffic into a dedicated default channel group. The goal is to make it easier for site owners to identify and compare AI-driven visits against traditional sources like organic search and direct traffic.

(Source: SEO Roundtable)

Google drops remaining FAQ rich results from Search

Google has removed any remaining FAQ rich results from appearing in Search. The feature has already been heavily restricted for most websites since 2023. The change also includes the phasing out of FAQ reporting in Search Console, with additional tooling support expected to be removed later in the year. 

FAQ structured data remains valid, but no longer produces visible SERP enhancements. The news marks another step in Google’s gradual reduction of traditional rich result features in favor of AI-driven experiences.

(Source: Search Engine Land)

AI search rewards content that goes deeper, says Google’s Nick Fox at Google Marketing Live 2026

In a Semafor interview about the future of AI search mechanics at this month’s 2026 Google Marketing Live event, Google’s Nick Fox (SVP of Knowledge & Information) emphasized that the proliferation of generative AI summaries heightens the algorithmic premium on “deep content.” Fox noted that while AI answers handle simple informational queries efficiently, they can’t replace rich, original human perspectives. As user behavior trends toward longer, more conversational queries, the AI systems seeks out primary evidence from external sources.

Fox’s advice for GEO/AEO? In the interview (around the 15-minute mark), he says:

“The biggest piece of advice we give is that the way to optimize for AI search is the same way to optimize for search: create great content that, as users, you would want. The additional piece of advice we give is: go beyond the surface level.”

“If you assume that the AI will provide sort of a first-level response or high-level framing, the content that will do the best within AI is one that goes one level deeper [or] two levels deeper and is really helpful.”

   
For content marketers and SEO pros, the takeaway is clear: Google continues to actively optimize its ranking systems to prioritize unique information that AI models cannot easily replicate. Content that relies on generic synthesis faces declining visibility, while firsthand data, unique brand perspectives, and expert analysis form the foundation for visibility in the AI era.

Watch the interview from Google Marketing Live 2026 here:

(Sources: Semafor ;  Search Engine Land ; Google Marketing Live 2026 interview video)

Snap ends its planned $400M Perplexity AI search deal

TechCrunch reported earlier this month that Snap and Perplexity “amicably ended” a previously announced $400 million deal that would have integrated Perplexity’s AI search engine into Snapchat. The partnership had been positioned as a way to bring conversational answers directly into Snapchat’s chat interface, but Snap said the companies had not agreed on a path to broader rollout.

For AI search watchers, the reversal is a useful reminder that distribution deals will be a major battleground — but not every AI search partnership will stick. Perplexity’s answer-engine model remains highly relevant to GEO/AEO, yet this update shows that embedding AI search into large consumer platforms can be commercially and operationally complicated. For brands, the broader lesson is to track not only standalone AI search products, but also where those products gain or lose distribution inside social, browser, and app ecosystems.

(Source: TechCrunch)

OpenAI releases GPT-5.5 Instant as ChatGPT’s new default model

This month, OpenAI announced that GPT-5.5 Instant will now be ChatGPT’s new default model, saying it is designed to provide smarter, clearer, more accurate, and more personalized responses. OpenAI also highlighted improvements in factuality, image understanding, STEM questions, and the model’s ability to decide when to use web search.

Per the OpenAI announcement:

“GPT‑5.5 Instant is a generally smarter model that’s more capable across everyday tasks, including improvements in analyzing photo and image uploads, answering STEM-related questions, and deciding when to use web search to provide a more useful answer.”

For GEO and AI search practitioners, the “when to search the web” piece is particularly important. As ChatGPT becomes better at deciding when fresh external information is needed, the relationship between model behavior, source retrieval, and content citation becomes more strategically important. Brands that want to be discovered in ChatGPT environments need to consider whether their content is accessible, authoritative, up to date, and useful enough to be retrieved when the model determines that web grounding is needed.

(Source: OpenAI announcement)

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The post AI Search & SEO Industry News – May 2026: Google I/O, the May Core Update, and More appeared first on Lumar.

Your content needs to build a case, not just make a claim

AI retrieval systems don’t just reward semantic relevance—they reward structured relevance and logical reasoning pathways. 

Content that clearly signals its intent, preserves entity and entity relationship clarity across sections, and builds connected reasoning paths is more resilient in AI retrieval environments and more likely to be incorporated into generated answers.

The idea that content structure can impact AI selection likelihood was a key finding in last year’s research paper, “What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context for Multi-Hop QA” (Chang, et al.). 

This research on retrieval-augmented LLMs shows that AI models perform best and deliver more accurate responses when the external content being considered forms a coherent, logically structured “chain of evidence” (CoE) rather than a loose collection of related facts.

Characteristics of ‘Chain of Evidence’ (CoE) content

You can think of the Chain of Evidence (CoE) approach as a way to ensure your content does more than just mention the right topic. It should clearly connect the user’s question to the answer through a logical path of supporting information.

A CoE-aligned piece of content has three main characteristics:

1. Clear intent alignment: The content understands and addresses what the user is really trying to find out. It does not just match the keywords in the query; it addresses the answer type or outcome the user is looking for.
2. Strong evidence nodes: The content includes the key entities, concepts, or facts needed to answer the question. These are the “stepping stones” an AI system or reader needs to move from the query to the answer.
3. Explicit evidence relationships: The content makes the connections between those entities clear. It explains how one fact relates to another, rather than leaving the reader or AI system to infer the logic.

The paper’s authors demonstrate that when the external knowledge provided to the LLM at inference time is ‘noisy’ or contains lots of irrelevant information, AI models’ outputs are significantly more accurate when the retrieved content exhibits CoE characteristics.

So, when optimizing your content for GEO/AEO, you’re not just aiming to boost relevance alone. You also need to structure your relevant points so that the pieces of evidence relating to your claims support each other in a logical chain. 

How can CoE content influence GEO/AEO?

In a RAG environment where an AI system might pull 10 different snippets from 10 different websites, the one with the strongest “chain of evidence” reasoning structure is the one most likely to survive the “noise” and get cited in the final generated response.

When these structural content elements are present together, LLM model accuracy remains more stable—even as irrelevant or conflicting information increases. 

Structuring your content as a “chain of evidence” can also help preserve brand accuracy in AI-generated responses. Per the research paper, if the external knowledge the LLM is relying on to form its generative response exhibits these CoE characteristics, “it can better resist interference from extraneous and even inaccurate information.”

(Note: The researchers found that the same CoE content structures can also make incorrect information more persuasive to AI systems if it appears logically coherent. This suggests that content structure may influence AI reasoning, regardless of whether the facts are correct. This is why ‘LLM-as-a-judge’ content evaluation guardrails are particularly important in ensuring generative AI platforms can deliver the best possible responses to users — more on that in our “LLM-as-a-Judge: How to Become a Preferred Content Source for AI Answers” post.)

Optimizing content for AI search visibility and LLM citations isn’t just about answering one prompt. It’s about building content that creates a clear chain of evidence: connecting entities, claims, supporting facts, definitions, and context in a way that helps AI systems follow the reasoning and understand why your answer is trustworthy and complete.

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About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

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The post Creating “Chain of Evidence” (CoE) Content for GEO / AEO appeared first on Lumar.

To be selected and cited by LLMs, a piece of content must first be unmistakably relevant to the query.

Semantic relevance refers to how closely your content matches the underlying meaning and intent of a user’s query — not just the exact keywords used in a prompt. (This is because, rather than exactly matching keywords in a user’s query to a piece of content, AI retrieval systems rely heavily on vector embedding-based semantic similarity and hybrid retrieval models to determine which passages are conceptually aligned with a user’s question.)

For generative engine optimization (GEO, also known as AEO), you need to think beyond keywords. Establishing broader semantic relevance and answer “completeness” in your content matters more than ever because AI search systems often perform a “query fan-out” when generating responses to a prompt. This means that the AI expands a single user question into multiple related sub-queries to gather broader context before generating an answer.

“AI often expands a single query into multiple sub-queries. Structuring content in tightly knit topical clusters ensures your brand becomes the authoritative source across the entire topic, capturing both the main question and all related follow-ups.”

— Stephen Akadiri, Senior SEO & Organic Growth Specialist

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This post is an excerpt from our in-depth guide to generative engine optimization (GEO) and answer engine optimization (AEO). Get the FULL 80-page AI search playbook for free here.

For GEO, semantic relevance is more impactful than stylistic choices in content

In a paper by UC Berkeley academics, “What Evidence Do Language Models Find Convincing?”, researchers showed that LLMs tend to overrely on semantic relevance and ignore many stylistic features of text that humans often deem important when assessing a document’s credibility. The team’s experiments showed that when AI models are forced to choose between two conflicting paragraphs and instructed to use only the provided text, relevance signals dominate the AI’s reasoning.

The stylistic elements that appeared to be largely ignored by LLMs in this research included language aspects such as:

• a neutral/objective tone of voice 
• confident or assertive language 
• domain-specific terminology and technical terms 
• lexical diversity (rich vocabulary) 
• sentence length, or word complexity

What the AI models prioritize instead:

In contrast, the research found strong positive effects on LLM selection from:

• question–paragraph vector embedding similarity 
• n-gram overlap with the question 
• explicit relevance boosts (e.g., pre- fixing with: “The following text is about the question: [question]”)

These relevance-based modifications significantly increased paragraph win-rate. (Note, in the context of this experiment, a paragraph “wins” when the AI model’s generated answer aligns with that source paragraph’s position in a head-to-head comparison against another paragraph on the same topic.)

In short, simple relevance features correlated much more strongly with which evidence the AI model “believes” (and then serves to users) compared to the more stylistic elements of a text.
Which is to say that semantic relevance seems to provide the strongest influence on LLMs’ judging and selection.

Learn more about optimizing your content for AI search visibility in our introduction to Content GEO.

How to optimize your content for semantic relevance

Semantic relevance goes well beyond the simple keyword-matching SEO tactics of yesteryear; it means ensuring your content matches the user’s true intent behind the prompts they input into AI systems. By mirroring actual user language, comprehensively answering questions, and expanding on related concepts, you move beyond the old keyword-stuffing approach to become a more authoritative source for a given query in the AI search landscape.

Semantic relevance optimization checklist

□    Lead with the question in the reader’s words. Use the terms and near-synonyms that appear in real user queries that your content can help answer. 

□   Answer early, then justify. Put a direct “Yes/No/It depends” style thesis near the top, followed by supporting reasoning. 

□   Optimize for hybrid retrieval by combining lexical precision with semantic expansion. Lexical precision reinforces exact-term and chunk-level matching, while semantic breadth increases recall by covering how different users might phrase related queries. Introduce related terms intentionally to widen your retrieval footprint without weakening entity clarity. 

□   Use a tight topical focus per section. Avoid burying the answer inside unrelated background. Keep each section focused on one claim or sub-question to improve: passage-level retrieval, chunk-level assessment, AI citation precision, and featured snippet capture. (Think in terms of ‘retrievable modules’ of content.) 

□   Optimize for passage-level clarity. Use specific named entities within each passage instead of pronouns (it, they). This helps ensure the passage is “complete” contextually, even when pulled in isolation. 

□   Make content scope explicit. Time bounds, geographies, and scope definitions (“in 2026,” “in the UK,” “for healthy adults”) reduce ambiguity and help retrieval match accurately. 

□   Cover counterarguments explicitly. AI systems frequently summarize both sides of contentious topics. If you don’t address counterarguments, another source will. Include: what critics say on the topic, where evidence is weak, and what remains uncertain. This improves topical completeness, balanced retrieval likelihood, and resistance to one-sided citation bias.

Further Reading

Semantic Relevance & SERP Rankings

Learn more about how semantic relevance also impacts traditional SEO today in our eBook, “SEO Research Study: Semantic Relevance and Search Engine Rankings.”

Passage-level semantic relevance

Many AI systems retrieve and synthesize information at the passage (or “chunk”) level.  If your content is semantically relevant at the passage level, clearly signaling what each section or module of your content is about and keeping core entities clear throughout each passage, it may improve your chances of getting that passage cited. That said, there’s quite a bit of disagreement about “content chunking” approaches in GEO/AEO…

On the content chunking debate in GEO / AEO

While Google has said that content chunking is unnecessary for appearing in its own AI systems, it’s worth understanding the concept, why it’s an often-discussed tactic in GEO/AEO, and why optimizing content at the “chunk” level might still matter for visibility in other AI systems.

Content chunking & AI extractability

Many AI systems do not “read” your entire page at once during their information retrieval phase. Instead, they utilize a process called chunking — breaking documents into smaller, discrete segments that can be independently indexed and retrieved.

AI sometimes uses a content chunking method because LLMs often operate on ultra-large knowledge bases, containing more “tokens” of contextual data and content than can be easily included in a single prompt, thus requiring a more scalable RAG (Retrieval-Augmented Generation) system.

Per Anthropic:

“RAG works by preprocessing a knowledge base using the following steps:

• Break down the knowledge base (the “corpus” of documents) into smaller chunks of text, usually no more than a few hundred tokens; [*Editor’s Note: 100 tokens = roughly 75 words.] 
• Use an embedding model to convert these chunks into vector embeddings that encode meaning; 
• Store these embeddings in a vector database that allows for searching by semantic similarity.
• At runtime, when a user inputs a query to the model, the vector database is used to find the most relevant chunks based on semantic similarity to the query. Then, the most relevant chunks are added to the prompt sent to the generative model.”

Critically, the retrieval step for some AI systems operates at the passage level, not the page level — typically retrieving sections of 100-300 words that semantically match the query. 

Passage-level retrieval is common because it is often more efficient and relevant than sending an entire long document into a model for every query. LLMs have context window limits, so feeding an entire 3,000-word article into the model for every query would be computationally expensive and often irrelevant. Instead, many AI retrieval systems surface only the most relevant passages, which are then used as context for answer generation. This means your content’s internal structure can influence how easily individual sections are interpreted, retrieved, and reused by systems that operate at the passage level. A 3,000-word article might have ten different sections that could be independently retrieved for ten different queries — but only if each section is structured to stand on its own.

Semantic chunking

According to research from Anthropic (“Introducing Contextual Retrieval in AI Systems”), standard RAG (Retrieval-Augmented Generation) systems often fail when individual content chunks lack sufficient context, leading to “failed retrievals” or hallucinations:

“In traditional RAG, documents are typically split into smaller chunks for efficient retrieval. While this approach works well for many applications, it can lead to problems when individual chunks lack sufficient context.”

For GEO, this means you should move beyond “fixed-size” chunking (splitting content into smaller chunks purely by a set number of characters or length) in favor of “Semantic Chunking”; organizing your content into semantically complete, coherent modules. This strategy ensures that each section of your content represents a “complete thought” that an AI can extract and use without losing the surrounding logic, making it significantly easier for AI “Judges” to verify your evidence chain. 

With semantic chunking, you’ll want to avoid the “pronoun penalty,” or using pronouns instead of specific entity names in any given chunk of content. 

As explained in the Anthropic research:

“A relevant chunk might contain the text: ‘The company’s revenue grew by 3% over the previous quarter.’ However, this chunk on its own doesn’t specify which company it’s referring to or the relevant time period, making it difficult to retrieve the right information or use the information effectively.”

This suggests that, if you want to make it as easy as possible for AI retrieval systems to understand our content, you should keep core entities visible throughout your content ‘chunks’. Repeat key entity names and terms consistently so the reasoning chain is easy to follow across individual content sections.

While pronouns may improve human readability, excessive substitution of pronouns for core entities (e.g., replacing “Steve Jobs” with “he,” or “the company” with “it”) can reduce the visibility of those entities within a passage. In AI retrieval systems that evaluate passages based on entity presence and relational clarity, this can weaken alignment with the query’s underlying reasoning structure.

While the Anthropic research on contextual retrieval for RAG systems was actually written to introduce a solution to this information chunk retrieval issue (“Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding”), and while Google says content chunking is unnecessary for visibility its own AI systems, you still might want to consider semantically chunking your content as a GEO best practice to help your content maintain utmost ease of retrieval across all AI systems, regardless of how sophisticated or contextually aware their RAG systems may be.

GEO and SEO experts still routinely echo the suggestion to optimize your content into semantically coherent chunks. After studying AI patents and research papers for two years, SEO and AI search thought leader Olaf Kopp wrote in a 2026 LinkedIn post that one of his key takeaways is:

“Structure content as self-contained, answer-first chunks so LLMs can find, extract, and cite precise information.”

Content chunking isn’t about gaming retrieval algorithms. It’s about recognizing that if some AI systems will only “see” one section of your article at a time, that section needs to be independently valuable and comprehensible.

This doesn’t mean arbitrarily or awkwardly breaking content up into fixed-length smaller parts. What you need is clear sectioning, for both human readers’ comprehension and information gain and machine-readability: use H2 and H3 headings to organize content into logical sub-topics, ensure each section addresses a distinct aspect of your subject, and write each section so it can be understood without requiring the reader (or the AI) to have read what came before.

After all, Google is not saying that content structure is irrelevant. Common, longstanding SEO and general writing advice also recommends organizing content into clear paragraphs, sections, and headers for human readers, too. Clear structure is good SEO, good UX, and likely helpful for some AI systems’ information retrieval processes.

In short: You probably don’t need to artificially “chunk” content into rigidly imposed, limited-length sections just because someone says LLMs prefer tiny blocks (especially for Google’s AI systems). But do structure and segment content clearly because readers, crawlers, accessibility tools, snippets, and many AI retrieval systems all benefit from passage-level clarity.

In other words: don’t “chunk” content purely as a GEO hack. Write clear sections for humans that are also easy for retrieval systems to understand.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

Free GEO / AEO eBook

Get the full GEO/AEO guide

Reminder: This article is an excerpt from our in-depth guide to AI search optimization. Get the FULL 80-page AI search playbook (it’s free!) here.

The post Semantic Relevance for GEO / AEO: How to Align Content with AI Search Intent appeared first on Lumar.

While Google has said that content chunking is unnecessary for appearing in its own AI systems, it’s worth understanding the concept, why it’s an often-discussed tactic in GEO/AEO, and why optimizing content at the “chunk” level can still matter for visibility.

Content chunking & AI extractability

Many AI systems do not “read” your entire page at once during their information retrieval phase. Instead, they utilize a process called chunking — breaking documents into smaller, discrete segments that can be independently indexed and retrieved.

AI sometimes uses a content chunking method because LLMs often operate on ultra-large knowledge bases, containing more “tokens” of contextual data and content than can be easily included in a single prompt, thus requiring a more scalable RAG (Retrieval-Augmented Generation) system.

Per Anthropic:

“RAG works by preprocessing a knowledge base using the following steps:

• Break down the knowledge base (the “corpus” of documents) into smaller chunks of text, usually no more than a few hundred tokens; [*Editor’s Note: 100 tokens = roughly 75 words.] 
• Use an embedding model to convert these chunks into vector embeddings that encode meaning; 
• Store these embeddings in a vector database that allows for searching by semantic similarity.
• At runtime, when a user inputs a query to the model, the vector database is used to find the most relevant chunks based on semantic similarity to the query. Then, the most relevant chunks are added to the prompt sent to the generative model.”

Critically, the retrieval step for some AI systems operates at the passage level, not the page level — typically retrieving sections of 100-300 words that semantically match the query. 

Passage-level retrieval is common because it is often more efficient and relevant than sending an entire long document into a model for every query. LLMs have context window limits, so feeding an entire 3,000-word article into the model for every query would be computationally expensive and often irrelevant. Instead, many AI retrieval systems surface only the most relevant passages, which are then used as context for answer generation. This means your content’s internal structure can influence how easily individual sections are interpreted, retrieved, and reused by systems that operate at the passage level. A 3,000-word article might have ten different sections that could be independently retrieved for ten different queries — but only if each section is structured to stand on its own.

Semantic content chunking

According to research from Anthropic (“Introducing Contextual Retrieval in AI Systems”), standard RAG (Retrieval-Augmented Generation) systems often fail when individual content chunks lack sufficient context, leading to “failed retrievals” or hallucinations:

“In traditional RAG, documents are typically split into smaller chunks for efficient retrieval. While this approach works well for many applications, it can lead to problems when individual chunks lack sufficient context.”

For GEO, this means you should move beyond “fixed-size” chunking (splitting content into smaller chunks purely by a set number of characters or length) in favor of “Semantic Chunking”; organizing your content into semantically complete, coherent modules. This strategy ensures that each section of your content represents a “complete thought” that an AI can extract and use without losing the surrounding logic, making it significantly easier for AI “Judges” to verify your evidence chain. 

Learn more about how semantic relevance impacts AI search results in our recent GEO/AEO explainer article.

Keeping entities clear across sections

With semantic chunking, you’ll want to avoid the “pronoun penalty,” or using pronouns instead of specific entity names in any given chunk of content. 

As explained in the Anthropic research:

“A relevant chunk might contain the text: ‘The company’s revenue grew by 3% over the previous quarter.’ However, this chunk on its own doesn’t specify which company it’s referring to or the relevant time period, making it difficult to retrieve the right information or use the information effectively.”

This suggests that, if you want to make it as easy as possible for AI retrieval systems to understand our content, you should keep core entities visible throughout your content ‘chunks’. Repeat key entity names and terms consistently so the reasoning chain is easy to follow across individual content sections.

While pronouns may improve human readability, excessive substitution of pronouns for core entities (e.g., replacing “Steve Jobs” with “he,” or “the company” with “it”) can reduce the visibility of those entities within a passage. In AI retrieval systems that evaluate passages based on entity presence and relational clarity, this can weaken alignment with the query’s underlying reasoning structure.

While the Anthropic research on contextual retrieval for RAG systems was actually written to introduce a solution to this information chunk retrieval issue (“Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding”), and while Google says content chunking is unnecessary for visibility its own AI systems, you still might want to consider semantically chunking your content as a GEO best practice to help your content maintain utmost ease of retrieval across all AI systems, regardless of how sophisticated or contextually aware their RAG systems may be.

GEO and SEO experts still routinely echo the suggestion to optimize your content into semantically coherent chunks. After studying AI patents and research papers for two years, SEO and AI search thought leader Olaf Kopp wrote in a 2026 LinkedIn post that one of his key takeaways is:

“Structure content as self-contained, answer-first chunks so LLMs can find, extract, and cite precise information.”

Content chunking isn’t about gaming retrieval algorithms. It’s about recognizing that if some AI systems will only “see” one section of your article at a time, that section needs to be independently valuable and comprehensible.

This doesn’t mean arbitrarily or awkwardly breaking content up into fixed-length smaller parts. What you need is clear sectioning, for both human readers’ comprehension and information gain and machine-readability: use H2 and H3 headings to organize content into logical sub-topics, ensure each section addresses a distinct aspect of your subject, and write each section so it can be understood without requiring the reader (or the AI) to have read what came before.

After all, Google is not saying that content structure is irrelevant. Common, longstanding SEO and general writing advice also recommends organizing content into clear paragraphs, sections, and headers for human readers, too. Clear structure is good SEO, good UX, and likely helpful for some AI systems’ information retrieval processes.

In short: You probably don’t need to artificially “chunk” content into rigidly imposed, limited-length sections (especially for Google’s AI systems). But do structure and segment content clearly because human readers, crawlers, accessibility tools, and many AI retrieval systems all benefit from passage-level clarity.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

Free AEO / GEO eBook

The 2026 AI Search Playbook

Get our full, FREE guide to GEO / AEO strategies now.

GEO / AEO Tools

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Try Lumar’s comprehensive GEO platform, with tools for tech GEO, content GEO, and AI visibility tracking. Get started with a demo now.

The post Content Chunking & AI Extractability appeared first on Lumar.

Which external content sources are preferred by AI?

An academic paper updated last year (“From Generation to Judgment: Opportunities and Challenges of LLM- as-a-Judge”) surveyed the emerging field of ‘LLM judges’ (that is, AI systems built to evaluate, score, and ‘judge’ inputs — or their own outputs). One thing is clear from the research: AI systems are getting increasingly good at content evaluation.

Understanding how LLMs are currently used for evaluation across various use cases — and what criteria they excel (or fail) at assessing — can give us some insights into how AI platforms may evaluate and select citations from external content (like yours!).

AI researchers (see sources under ‘Further Reading’ below) have found that LLMs prefer content that is:

• Relevant to the questions users are asking 
• Coherent, well-evidenced, and logically structured 
• Validated by others (ie, highly cited or frequently mentioned by others) 
• Includes credible citations to others, substantiating its claims and trustworthiness
• “Helpful, honest, and harmless” (See notes on Claude’s ‘Constitution’, below)
• Complete in its topical coverage
• Recent 
• Easily extractable

“We need to understand how AI interprets our content and generates answers so we can adapt our strategies and ensure our content appears in results.”

— Ana Perez, SEO Manager

Why content quality matters more than ever in the age of AI search

Your content quality matters in AI search because generative systems rely heavily on retrieval signals, source reputation, and structured knowledge to decide which content to incorporate into their answers. Modern AI-powered search experiences do not generate responses from thin air — they draw from indexed web content, ranked sources, and retrieval-augmented pipelines. 

When determining which external sources to cite, summarize, or rely on, AI systems draw on many of the same credibility signals that underpin traditional ranking systems: domain authority, consistent authorship, factual alignment, and corroboration across trusted sources. 

In practice, high E-E-A-T (experience, expertise, authority, and trustworthiness) content is more likely to be retrieved, retained in context, and considered safe to reuse in AI-generated answers.

SEO professionals will already be very familiar with the concept of E-E-A-T. The idea stems from Google’s internal Search Quality Rater Guidelines, the massive manual used by thousands of human testers to grade Google’s search algorithm.

First popularized as a benchmark for “Your Money or Your Life” (YMYL) topics, E-E-A-T has evolved from a niche set of quality guidelines into a foundational pillar of how search algorithms identify and reward the most credible voices in any given field. 

E-E-A-T can also serve as a strong content framework for GEO/AEO, as AI search systems have an incentive to prioritize factually accurate, credible, trustworthy content in order avoid the reputational damage caused by LLM hallucinations (we’ve all seen plenty of examples of this in the press over the past few years!).

On content quality in the age of AI search:

“Content quality is always going to be a trending topic, but even more so in 2026. Brands that rely too heavily on AI to write copy will struggle to demonstrate genuine E-E-A-T. Google and OpenAI’s models are already learning to evaluate who is behind the content, why it exists, and what value it adds, so I would say content quality should be on everyone’s radar! “

—Chloe Steele, SEO Account Manager at Verde Digital

(Hear more from Chloe in our Lumar webinar session: “SEO & GEO Trends for 2026”) 

AI’s grading rubric: how LLMs can evaluate content quality today

To understand why some content is cited and others ignored, we must look at how AI evaluates quality. 

The LLM-as-Judge paper we mentioned earlier outlines the primary benchmarks that today’s AI systems can use to “judge” external inputs as well as their own outputs.

For a piece of content to survive the AI’s internal filtering process, it should score high across six critical areas:

• Helpfulness 
• Harmlessness
• Relevance 
• Reliability 
• Feasibility, and
• Overall Quality

These aren’t just abstract concepts; they are specific metrics an AI “judge” might use to decide if your content is authoritative enough to present to a user.

But are these AI-powered evaluations actually being implemented by public-facing AI platforms today? Yes, it seems so.

Anthropic’s Claude 2 Model Card, for example, discusses their work to train Claude on the three H’s; they have explicitly stated that they are training their LLM to be Helpful, Honest, and Harmless.

Per the Claude 2 Model Card document:

“Our core research focus has been training Claude models to be helpful, honest, and harmless. Currently, we do this by giving models a Constitution – a set of ethical and behavioral principles that the model uses to guide its outputs.”

“You can read about Claude 2’s principles in a blog post we published in May 2023 [See: Claude’s Constitution]. . . . We use the constitution in two places during the training process. During the first phase, the model is trained to critique and revise its own responses using the set of principles and a few examples of the process. During the second phase, a model is trained via reinforcement learning, but rather than using human feedback, it uses AI-generated feedback based on the set of principles to choose the more harmless output.”

Industry analysis of Google’s documentation suggests that its AI Overviews are not a fully separate silo; they are powered in part by some of the same core ranking systems as traditional search, including the “Helpful Content System.” If content is deemed “helpful” and demonstrates strong E-E-A-T according to Google’s search evaluation criteria, chances are, it’s also more likely to be considered a valuable source for AI summarization.

When it comes to other companies’ AI search tools, like those embedded in Microsoft Bing, we also have clues that these AI systems are being developed to prioritize trustworthy content. In Microsoft’s “Responsible AI” document, it underscores Microsoft’s commitment to “Fairness,” “Reliability & Safety,” and “Transparency” in its AI systems. For Bing’s Copilot to be reliable, safe, and transparent, the sources it uses for information should be demonstrably accurate and trustworthy.

Likewise, in Perplexity’s help articles, it states that: “When you ask Perplexity a question, it uses advanced AI to search the internet in real-time, gathering insights from top-tier sources.” — Again, content quality and E-E-A-T signals are at work here.

“SEO best practices remain essential even as AI search grows. Focus on building brand visibility across multiple channels, creating AI-friendly content with structured headings and quotable insights, researching real user questions, and incorporating first-party data and unique perspectives. Above all, make your content genuinely helpful.”

— Ana Perez, SEO Manager

How to improve EEAT for GEO

As AI-generated content floods the web, human expertise is becoming a differentiating factor rather than a baseline expectation. LLMs are increasingly trained to recognize who produced content, why it exists, and what real-world experience backs it up. Surface-level content is increasingly filtered out in favor of genuinely authoritative sources.

As Jon Clark, Managing Partner at Moving Traffic Media, puts it:

On taking E-E-A-T to the next level:

“With all this AI-generated content spilling out onto the web, human expertise is about to become a major differentiator. Again. It’s the un-apologetic authors, the credentials that stand up to scrutiny and the real hands-on experience that sets the regurgitated reviews and surface-level tat apart from genuine authority.

[In] 2026, both Google and the AI systems they’re building will be looking for the all-important trust credentials – where did that insight come from, and who put their name on it.”

• (Note: You can hear more from Jon in his Lumar GEO/AEO webinar session, “The New Metrics of AI Search – GEO/AEO KPIs You Should Track Now.” )

Get EEAT Analytics in Lumar

Our AI Authority / E-E-A-T reports in Lumar can help you ensure your content signals expertise, experience, authority, and trustworthiness. 

Get a Lumar GEO platform demo to see our E-E-A-T reports in action— alongside our many other GEO/AEO reporting and analytics tools, like our GEO Content Evaluator.

Content Quality Checklist for GEO / AEO

Practically, creating high-quality, strong E-E-A-T content for generative engine optimization means incorporating the following into your content production workflows:

□   Named authorship with verifiable credentials — Content attributed to real experts with demonstrable experience signals trust to both human readers and AI systems evaluating source quality.

□  First-party data and original research — Proprietary statistics, surveys, and case studies give AI systems unique, citable material that they cannot source elsewhere.

□  Factual accuracy and consistency — Contradictions between pages on your own site, or between your content and established facts can erode AI trust in your brand as a reliable source.

□   Transparent sourcing — Citing reputable external references, studies, and named experts reinforces the factual grounding of your content.

New Lumar tools for GEO content optimization

Explore Lumar’s powerful platform features for GEO content optimization:

• GEO Content Evaluation Tools
• AI Visibility Tracking Tools
• The Full Lumar GEO / AEO Platform

Get Lumar’s FULL guide to GEO / AEO

Ready to dive deeper into how to build a strong GEO / AEO strategy for AI search? Get our full, free “AI Search Optimization Playbook” now.

The Ultimate Guide to GEO

Get the full GEO/AEO playbook

This post is an excerpt to our 80-page guide to GEO / AEO – Get the FULL AI Search Playbook for free here.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

The post LLM-as-a-Judge: How to Become a Preferred Content Source for AI Answers appeared first on Lumar.

An introduction to Content GEO

Content GEO is about ensuring your content is built for both human understanding and AI retrieval, assessment, selection, and inclusion. It requires focus on publishing factually accurate, well-structured, semantically relevant, and comprehensive content that AI systems can extract, summarize, and cite confidently. 

For human readers, your content still needs to be clear, useful, accurate, persuasive, and easy to navigate. 

For AI systems, your content also needs to be structured in a way that makes its meaning, relevance, evidence, and expertise easy for machines to identify.

That means Content GEO is not about writing robotic copy or chasing GEO/ AEO “hacks.” It is about making your best expertise easier for machines to understand without making it less useful for people.

A good Content GEO strategy should address questions like:

• Is this content clearly about the topic it claims to cover?
• Does it answer the user’s question directly?
• Does it contain accurate, specific, and useful information?
• Does it show why the brand is qualified to speak on this topic?
• Can an AI system extract a useful answer from this page without misrepresenting it?
• Does the content strengthen the brand’s association with the topics it wants to own?

If Technical GEO determines whether your content is accessible to AI bots, Content GEO determines whether it is deemed useful, trustworthy, and likely to be chosen. It ensures that once an AI system can access your content, recognize your brand, and trust your authority (see also: Entity GEO and Brand Authority GEO), it has the right material to use when generating its responses for users.

Why It Matters Now: As AI-generated “slop” floods the web, LLMs are being trained to prioritize “helpful, honest, and harmless” content (the 3 Hs!). To survive the AI’s internal filtering process, your content must score high in helpfulness, reliability, and overall quality.

The Ultimate Guide to GEO

Get the full GEO/AEO playbook

This post is a companion piece to our 80-page guide to GEO / AEO – Get the FULL AI Search Playbook for free here.

Differences between SEO content and GEO content

Where traditional SEO content aimed to align with keyword queries, Content GEO focuses on satisfying AI reasoning — ensuring your brand’s insights, explanations, and expertise become the material LLMs rely on when generating answers.

In an AI-driven search landscape, your content adds yet another dimension to its role in your broader marketing toolkit; it becomes training material for the models shaping the future of online search.

“We need to understand how AI interprets our content and generates answers so we can adapt our strategies and ensure our content appears in results.”

— Ana Perez, SEO Manager

Traditional SEO content has often been built around ranking for a keyword and earning the click. Content GEO expands that goal.

In AI search, visibility may happen before, after, or without a website visit. Your content might influence an AI-generated answer even when the user does not click. Your brand might be cited directly, mentioned without a link, summarized as part of a recommendation, or omitted entirely.

That means content teams need to think beyond traffic alone. Content can now shape:

• AI understanding: Whether AI systems correctly understand what your brand knows, offers, and stands for.
• AI inclusion: Whether your content is selected as part of an AI-generated response.
• AI citation preference: Whether your content is chosen over a competitor’s when multiple sources could answer the same question.
• Brand-topic association: Whether AI systems consistently connect your brand with the topics that matter to your category.
• Answer accuracy: Whether AI systems describe your brand, products, services, and expertise correctly.

This does not make traditional SEO obsolete. Strong SEO fundamentals still matter. Many AI search experiences rely on traditional search indexes, established content signals, and web-wide authority signals. But Content GEO asks content teams to build pages that are not only optimized for ranking and reading, but also for retrieval and reuse.

Content SEO and Content GEO overlap, but they are not identical.

Traditional content-focused SEO tactics often start with a search query, keyword opportunity, ranking gap, or traffic goal. The main question is: How do we create the best page to rank and earn the click?

Content GEO starts with a slightly different question: How do we create the best source for an AI system to understand, trust, and use when generating an answer?

That shift changes the content brief.

A traditional SEO-focused content brief might emphasize target keywords, search volume, H1s, title tags, internal links, and SERP competitors. A GEO content brief still includes those considerations, but it also asks for clearer answers, more explicit definitions, stronger evidence, better topical alignment, visible expertise, and page sections (or ‘chunks’) that make sense when interpreted outside of the full page context.

The best Content GEO work does not abandon SEO. It raises the quality bar.

Where Content GEO fits in the AI visibility funnel

Content GEO sits primarily in the AI Understanding and AI Inclusion stages of the AI visibility funnel.

At the discovery stage, technical factors determine whether AI bots and search crawlers can access your content. That is the domain of Technical GEO.

But once your content can be accessed, the next questions are different:

• Can AI systems understand what this content is about?
• Can they connect it to the right topics and entities?
• Can they identify a useful answer?
• Can they evaluate the content as accurate, helpful, and trustworthy?
• Can they confidently include it in a generated response?

Those are Content GEO questions.

This is why Content GEO cannot operate in isolation. It works best when the other GEO pillars are already in motion. If AI cannot crawl your page, the content may not be discovered. If AI cannot recognize your brand as an entity, the brand may not be properly associated with the content. If AI does not see enough authority signals around your brand, even strong content may lose out to a more trusted source.

Content GEO is the layer that turns access and authority into usable answers.

“In 2026, GEO/AEO will be essential for visibility, requiring content to be highly structured, fact-based, and semantically rich to be considered authoritative enough for AI to reference.

Rather than chasing rankings alone, SEOs must now optimise for inclusion in zero-click, conversational responses — ensuring that brand-owned content is answer-ready and aligned with user intent at a granular level.”

— Rejoice Ojiaku, Senior Content Specialist at Wise

(Check out Rejoice’s Lumar webinar session: “SEO in the Age of AI: A Conversation on GEO, AEO & the Future of Search.”) 

How Content GEO influences AI search visibility

A strong Content GEO strategy can improve four key aspects of AI search visibility.

1. Extractability

Extractability is how easily an AI system can pull a clear, factual answer from your page.

Content that buries the answer under long intros, vague claims, or disconnected paragraphs is harder to reuse. Content that states the answer clearly, defines key terms, and explains the surrounding context is more likely to support AI-generated responses.

2. Citation preference

Citation preference is whether an AI system chooses your content instead of another source.

For competitive topics, AI systems often have multiple possible sources to draw from. Your content needs to give them a reason to prefer yours: clearer explanations, stronger evidence, better specificity, fresher information, original insights, or more complete coverage.

3. Topical association

Topical association is how consistently AI systems connect your brand with a specific subject area.

One optimized article rarely builds durable AI visibility on its own. Content GEO works best when individual pages support a broader topical footprint. Over time, your content should help AI systems understand what your brand is known for.

4. Completeness

Completeness is whether your content covers enough of the topic to satisfy the user’s real question and likely follow-up questions.

AI search experiences are conversational. A single prompt may imply several related needs. Content that only answers the narrowest version of a query may be less useful than content that anticipates the user’s next question, defines scope, addresses nuance, and points toward the right next step.

What Content GEO is not

Content GEO is not keyword stuffing for AI. It is not adding an FAQ block to every page and calling the job done. It is not generating thousands of generic articles to “feed” LLMs.

In fact, generic AI-written content can make the problem worse. If every brand publishes the same surface-level explanations, AI systems have little reason to choose one source over another.

Content GEO is about making your content more distinctive, more useful, more structured, and more grounded in real expertise.

That can include original research, expert commentary, unique customer insights, product knowledge, practical examples, data, comparisons, and clearly sourced claims. 

The goal is not simply to produce more content. The goal is to produce content that AI systems can confidently interpret as useful source material.

Your Content GEO checklist

The later posts in this GEO/AEO series will cover specific Content GEO tactics in depth. For now, use this as a strategic overview of what your GEO content program should be working toward: 

□  Write clear, factual, answer-first content that directly addresses user questions.

□  Optimize for semantic relevance and full coverage of related sub-topics.

□  Build evidence-based content with citations and sources.

□  Structure content into easily retrievable “chunks” (clear sections with H2/H3 headings.

□  Ensure entity clarity in ‘chunk’ passages (avoid pronouns replacing key entities).

□  Create multimodal content (video, audio, images) and add transcripts to video/audio; descriptive alt text to images.

□  Demonstrate E-E-A-T (Experience, Expertise, Authority, Trustworthiness) through expert authorship, original data, and transparent sourcing.

“The way forward is to treat content as a cross-platform ecosystem. Make videos, images, and audio AI-friendly with proper metadata, captions, and transcripts.

Ensure entity and topic alignment so AI systems recognize your authority, no matter the format.”

— Jon Clark, Managing Partner at Moving Traffic Media

• (Hear more from Jon in his Lumar GEO/AEO webinar session, “The New Metrics of AI Search – GEO/AEO KPIs You Should Track Now.” )

The bottom line: content is becoming AI source material

Content has always helped brands educate audiences, build trust, and drive demand. In the AI search era, it has another role: it becomes source material for the systems shaping how people discover, compare, and choose brands.

That does not mean every page should be written for machines first. It means content teams need to recognize that machines are now part of the audience. The strongest content will serve both: useful enough for humans to value, and clear enough for AI systems to understand, retrieve, and cite.

The brands that win in AI search will not be the ones that publish the most. They will be the ones that make their expertise easiest to understand, verify, and reuse.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

Explore Lumar’s powerful platform features for GEO content optimization

• GEO Content Evaluation Tools
• AI Visibility Tracking Tools
• The Full Lumar GEO / AEO Platform

Get Lumar’s FULL guide to GEO / AEO

Ready to dive deeper into how to build a strong GEO / AEO strategy for AI search? Get our full, free “AI Search Optimization Playbook” now.

The post How to Optimize Your Content for AI Search Visibility appeared first on Lumar.

1. Technical GEO
2. Content GEO
3. Entity GEO
4. Brand Authority GEO (what we’ll be discussing here!)

What is brand authority GEO?

Brand Authority GEO is about building credibility, trust, and preference. Once AI recognizes your brand as an entity, it must decide whether your entity deserves to be included, cited, or recommended in its generative responses.

“Gaining visibility in AI isn’t about optimization—it’s about creating brand and trust signals so strong that AI would be foolish not to put you front and center.”

— Natasha Burtenshaw-deVries, Acquisition Marketing Manager at Well.ca 

The Ultimate Guide to GEO

Get the full GEO/AEO playbook

This post is an excerpt from our huge, 80-page guide to GEO / AEO – Get the FULL free AI Search Playbook here.

Why Brand Authority GEO matters

Why does building brand authority matter for generative engine optimization? How your brand appears across the web (not just on your own website!) influences:

• Whether AI trusts the content you produce 
• Whether your brand, its content, and its perspectives are selected for inclusion in AI answers over your competitors’ 
• How prominently your brand appears in AI answers
• The tone/brand sentiment of your AI mentions (neutral vs favorable vs negative)

How generative AI uses brand authority signals

Brand authority signals help AI models answer higher-order questions during their consideration of potential sources to use when constructing a generative answer, such as:

• Is this entity reliable?
• Is this entity respected within its field? 
• Do other trusted sources validate this entity? 
• Would citing this brand’s content improve answer quality?

Your brand’s perceived reputation — how AI systems will ‘understand’ its credibility — is shaped by signals that go beyond your own website. This means multichannel and multimodal strategies have become increasingly important in the age of AI search.

On trust and authenticity signals:

“As AI tools, platforms and processes accelerate the amount of AI slop being pumped out into the world, finding a way to cut through the noise will be more critical than ever.

‘Just create great content’ has never been enough. In 2026 search and AI platforms will be looking for new ways to discern and understand authenticity and trust.

When everyone says, ‘build your brand,’ what they often mean is: ’be a voice people trust.’”

— Andrew Cock-Starkey, Founder and SEO Consultant at Optimisey

Brand Authority GEO tactics

Brand Authority GEO is driven by external validation and real-world trust signals, including:

• PR and media mentions 
• Influencer and expert citations
• Customer reviews and testimonials 
• Multichannel content consistency and visibility 
• Demonstrated leadership and expertise over time

“Good content alone is not going to move the needle anymore. We need strong brand authority. Things that have long been seen as “nice to have” or “bonus SEO” are now must-haves, such as Digital PR to generate positive brand sentiment and brand mentions.”

— Tina Reis, Senior SEO Strategist at Muhlert Digital

“Brand authority & reputation management [will be crucial skills to hone in 2026]. Why? To be cited and trusted, we need to clearly communicate who we are, what we offer, and how we solve problems. LLMs prioritize authoritative content with a clear and consistent brand identity. 

These changes will require continued investment in brand recognition, developing strategies that go beyond the website, and creating content across social media. It’s essential to listen to what users are saying about your brand in forums and incorporate those insights into your site.”

— Ana Perez, SEO Manager

Brand authority in the GEO Funnel 

Brand Authority GEO focuses on making sure your brand is recognized as a trustworthy source of information that is likely to be relied upon as a resource when AI systems are generating responses. This aligns to the ‘AI Inclusion’ stage in the GEO Funnel: making sure your brand and its content is optimized for AI selection and inclusion in AI answers.

On brand reputation management for AI visibility:

“As AI and LLMs evaluate brand credibility, SEOs (more than ever) will need skills in monitoring online reputation, managing reviews and UGC platforms to ensure that signals of authority and trust are accurately represented across search.”

—Chloe Steele, SEO Account Manager at Verde Digital

• (Hear more from Chloe in our Lumar webinar session: “SEO & GEO Trends for 2026”)

Key differences between Entity GEO & Brand Authority GEO

Elsewhere, we’ve discussed a sub-discipline of GEO we’re calling Entity GEO — which  might, at first glance, seem to overlap with Brand Authority GEO.  To avoid confusion, below we’re outlining some of the key differences between Entity GEO and Brand Authority GEO, so you can see how each of these aspects of AI search optimization requires different tactics and activities. 

Outcomes of a strong Brand Authority GEO plan

The goal of implementing a Brand Authority GEO plan is to optimize your brand’s online reputation so AI systems are more likely to: 

• Prefer your brand over less trusted entities 
• Cite your content directly 
• Mention your brand favorably in recommendations 
• Use your perspectives as default answers

 How to build brand authority for GEO

In traditional SEO, a whole sub-industry developed around securing backlinks — for GEO, even brand mentions alone without a “dofollow” backlink can help establish your brand as a trusted, authoritative entity.

This means digital PR is having a bit of a renaissance, with the importance of authoritative brand mentions from credible sources that go beyond your own company’s website rising in importance once again.

As Raquel Gonzalez Exposito, CEO & Founder at Seoulful Connect, explains:

“I think brand reputation and authority will be more important than ever in 2026. We’ve always talked about becoming a reference in your niche and getting backlinks from reputable sources, but now it’s even more important to showcase that expertise through your own data, expert quotes or insights, and contributions from real professionals in the field, all of which help strengthen your topical authority.

As we know, brand authority isn’t built overnight. Most of the brands already showing up in AI-generated responses are there because they’ve been doing the work for years and have earned that recognition. We’ve always focused on getting dofollow backlinks, and that’s still relevant, but simple brand mentions will also matter more now, since AI models can recognize and display those sources.

That’s because AI doesn’t just evaluate backlinks; it looks at mentions, reviews, EEAT consistency, and other credibility signals. So, strengthening your digital PR and content marketing efforts will be key. You should also check how AI tools mention your brand to understand how they perceive your business, and identify ways to improve both visibility and trust.”

Press mentions matter for building brand authority, but there are other brand credibility signals you can work with: think industry awards, third-party review websites, influencer mentions on social media, forum threads, and partnership marketing mentions on others’ websites and social profiles.

“Even when content isn’t directly cited via links, LLMs still recognize brand mentions across the web. Digital PR, thought leadership, and high-authority citations all improve your chances of surfacing in AI-driven summaries.”

— Rejoice Ojiaku, Senior Content Specialist at Wise

• (Check out Rejoice’s Lumar webinar session: “SEO in the Age of AI: A Conversation on GEO, AEO & the Future of Search.”) 

“PR becomes much more important when mentions are taken as an element of success and not just links.”

— Emina Demiri-Watson, Head of Digital Marketing at Vixen Digital

Your brand authority GEO checklist

□ Digital PR & Media Mentions: Secure unlinked and linked brand mentions from tier-1 industry news sites and major trade publications. (Signals: trust/co-citation)

□ Expert/Influencer Citations: Get cited or interviewed by established, authoritative experts and influencers in your niche. (Signals:

respect within field)

□ Positive Brand Sentiment: Actively manage and generate positive reviews on all key third-party platforms (e.g., Google, Yelp, G2, industry-specific review sites). (Signals: reliability/preference)

□ Industry Association & Awards: Become an accredited member of relevant trade bodies and win recognized industry awards. (Signals: validation by trusted sources)

□ Sponsorships & Partnerships: Partner with established, non-competitive entities to associate your brand with positive, high-authority signals. (Signals: legitimacy/ecosystem trust)

 “The way LLM-type search surfaces work means a much wider breadth of opinion is summarized, so a multi-site presence and consistency in what is being said about you is more important than ever.

This means that effective digital PR, getting people to talk about your brand, your story, your data, on trusted websites, with consistency will pay even bigger dividends than before, link or not.”

— Mark Williams-Cook, Director at Candour

• (Hear more from Mark in our Lumar webinar session: “SEO & GEO Trends for 2026”)

“I believe in having your brand mentioned (linked or unlinked) in contextual and topically relevant articles on websites other than your own to improve visibility in LLMs and AI search. The more your brand appears across the web, the easier it is for AI crawlers to find it — and the more likely you are to be cited or mentioned in AI searches and LLMs.”

. . . 

“Work on strengthening your brand alongside your content efforts. Allocate part of your SEO budget to Digital PR and securing brand mentions on sites other than your own to improve trust and authority for both Google and LLMs.”

— Katarina Dahlin, Senior Growth Hacker at WhitePress

The Ultimate Guide to GEO

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About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

The post Building Brand Authority for GEO / AEO appeared first on Lumar.

As in SEO, GEO strategies can be broken down into a few key pillars. Namely: 

• Technical GEO 
• Content GEO 
• Entity GEO 
• Brand Authority GEO

Here, we’re focusing on the fundamentals of Technical GEO — that means building a machine-readable foundation for your website and content so AI bots can easily access, understand, evaluate, and choose it for inclusion in their generated responses.

The Ultimate Guide to GEO

Get the FULL guide to GEO / AEO.

This post is an excerpt from our 80-page guide to GEO / AEO – Get the FULL free AI Search Playbook here.

First of all, what IS Technical GEO? 

Technical GEO ensures your website (and the content you host on it) is built in a way AI systems can access and process. It ensures your content can be discovered, rendered, and interpreted correctly by AI bots. 

This technical layer is the backbone that allows your Content, Entity, and Brand Authority GEO strategies to function further down the funnel; if AI cannot properly access or understand the primary source of information about your brand (your website), chances are, your brand will not be fully or accurately included in AI answers.

Why implement a Technical GEO strategy?

Tech GEO influences multiple aspects of the generative AI processes that takes a user’s prompt from asked to answered, including:

• AI Discoverability: How quickly AI and search engine bots discover new and updated content from your brand’s website. 
• Content Freshness: Ensuring the AI always extracts the most current version of your facts and data. 
• Citation Eligibility: Whether your content is technically eligible to be included in generated answers (ie, AI bots are not blocked by robots.txt or NoSnippet rules). 
• AI Renderability: Whether your pages render correctly when accessed by AI systems — necessary for AI to ‘read’ your content as intended.

Technical GEO’s place in the AI visibility funnel

The focal point of Technical GEO is AI discoverability — making sure your content is discoverable (& permitted for use) by AI systems. 

This aligns with the ‘Discovery’ stage in our GEO Funnel:

On Technical GEO:

“SEOs will need to understand how content is rendered [by AI systems] and ensure it is both crawlable and machine-readable.”

— Natalie Stubbs, Senior Technical SEO at Lumar

Key tips for Technical GEO / AEO audits

Before we dive deeper into key tactics for Technical GEO, familiarize yourself with some of the key activities you’ll need to cover as part of your Tech GEO plan (many of these will sound familiar to those who work in Tech SEO!):

Now, let’s dig into some of these key action-plan items to ensure your Technical GEO is in good shape…

Robots.txt: Are you blocking AI bots? 

For GEO, it is fundamental to ensure that AI bots can access your website’s content. If pages on your site are blocking AI bots (e.g., via robots.txt), these systems cannot crawl and process the content, thereby preventing it from appearing in AI-generated summaries and directly hindering your AI search visibility. 

If you want to appear in generative AI responses and AI search features, make sure your site isn’t unintentionally blocking important AI crawlers such as:

• GPTBot — If you want your brand or content to be cited in ChatGPT responses, you need to ensure you are not accidentally blocking OpenAI’s crawlers (like GPTBot) on your website.
• PerplexityBot — Perplexity is a major AI search engine. Blocking its crawler (PerplexityBot) from accessing your web pages can prevent your brand’s content from being included in its AI responses.
• Google-Extended — To appear in Google Gemini and other Google AI-related features, you should ensure your site is not blocking their AI-related crawlers (like Google-Extended).
• BingBot or BingPreview — Microsoft’s Bing search engine offers several controls available to limit inclusion in Bing Chat’s generative responses — check that your site is not accidentally set to block inclusion in Bing Chat, if you want your brand to appear there.
• CCBot — Common Crawl is a non-profit organization that continuously crawls the public web and freely provides its massive archives and datasets to the public. The data collected from these crawls is then made available in formats suitable for research and development. — Common Crawl is a foundational source of data for a significant number of AI models, particularly large language models (LLMs). It feeds many AI training datasets. Blocking the Common Crawl bot (CCBot) on your site limits AI models’ training exposure to your brand and content.

Make your content available to AI

Before AI bots can crawl and parse your pages for content that may be relevant to cite in their responses, the page must first be available for the bots to access. To enable your website’s content to be discovered and cited in AI-generated answers, make sure none of your target pages return availability errors such as client-side 4xx errors (like 404 errors) or server-side 5xx errors. Both traditional search and AI bots cannot crawl pages that are unavailable or unreachable, effectively excluding them from being referenced by generative models. 

Nosnippet issues for GEO

Are nosnippet tags preventing your content from appearing in AI responses?

In the era of GEO and AI search, the ability to control how your content is summarized is a powerful tool — or a critical issue if mismanaged.

The nosnippet directives (including the meta tag, X-Robots-Tag header, and data-nosnippet attribute) were originally designed to prevent search engines from creating rich text snippets in traditional search results.

Today, these same nosnippet directives are a direct signal to AI systems. By preventing a snippet from being generated, a nosnippet directive also prevents your content from being used as a direct input for AI-generated responses, AI Overviews, and other AI-powered SERP features.

The nosnippet meta tag is a crucial tool for GEO because Google has confirmed it prevents AI systems, including AI Overviews and AI Mode, from using a page’s content to generate AI-powered snippets and responses.

Per Google Search Central, the nosnippet directive tells Google:

“Do not show a text snippet or video preview in the search results for this page. A static image thumbnail (if available) may still be visible, when it results in a better user experience. This applies to all forms of search results (at Google: web search, Google Images, Discover, AI Overviews, AI Mode) and will also prevent the content from being used as a direct input for AI Overviews and AI Mode.”

And per Google Search Central’s documentation on appearing in Google AI features: 

“Technical requirements for appearing in AI features:To be eligible to be shown as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to be shown in Google Search with a snippet, fulfilling the Search technical requirements.”

JavaScript rendering issues for AI bots

What an AI bot “sees” is not always what a human sees — and may not even be what traditional search bots see. Content discrepancies between the raw HTML and the rendered page, which is processed after JavaScript is executed, can lead to a critical “content blind spot” for AI systems.

As Matt G. Southern reports in Search Engine Journal, while Googlebot is highly capable of rendering JavaScript, many other AI crawlers are not as advanced and still rely on the static HTML:

“Traditional search crawlers like Googlebot can read JavaScript and process changes made to a webpage after it loads … In contrast, many AI crawlers can’t read JavaScript and only see the raw HTML from the server. As a result, they miss dynamically added content…”

This creates a new layer of technical GEO to manage. If any of your key content — the headings, paragraphs, and data that provide content authority — is only loaded via JavaScript, your most valuable information may be invisible to AI platforms.

The challenge for GEO is to ensure that a website’s technical foundation is robust enough for all crawler bots (including AI crawlers), not just Google’s.

“I think AI is not a revolution in search; it’s just an extension. AI search strategies put more light on previously undervalued areas of SEO that some of us ignored or took for granted.

In some areas, it’s time to go back to basics, e.g., with rendering. Google told us for years that they manage to render pages well (I still have pretty large doubts), but now AI search crawlers again don’t render JavaScript, and we again need to optimize how we render content to make sure AI search crawlers can find it.

Making your site cheaper to both render and understand content is getting more and more important.”

— Patryk Wawok, Co-founder & Head of Technical SEO at Organic Hackers

Indexability & GEO

Website indexability issues (such as pages being non-indexable via robots.txt, incorrect use of canonical tags, or using the unavailable_after directive) don’t just impact your traditional SEO and SERP results — they also have a knock-on impact on AI search results and AI-generated answers.

As Barry Schwartz explains in Search Engine Land, “To show in Google AI Mode & AI Overviews, your page must be indexed.”

ChatGPT & Bing’s search index

If, previously, you were primarily concerned only with getting your content indexed by Google, it may be time to expand your scope here for GEO… because ChatGPT’s search function appears to rely heavily on Bing’s search index.

Per OpenAI’s announcement about ChatGPT Search:

“ChatGPT search leverages third-party search providers, as well as content provided directly by our partners, to provide the information users are looking for.”

And as Danny Goodwin writes in SEL, “. . . One of those third-party search providers is Microsoft Bing. Based on its ChatGPT search help document, OpenAI is sharing search query and location data with Microsoft.”

Structured data & GEO 

How structured data may impact AI visibility: 

While the core training of large language models (LLMs) used in AI bots primarily relies on vast quantities of unstructured data and text, many AI-powered search features (like Google’s AI Overviews) that synthesize information from existing search engine indices may also indirectly benefit from structured data. This is because structured data helps traditional search engines better understand, categorize, and surface relevant content, which in turn can improve the chances of that content being selected and leveraged by the AI for its generative responses. 

LLMs are trained on massive datasets of text; predominantly unstructured human language (books, articles, web pages). Most AI systems’ “understanding” of context primarily comes from patterns in natural language, not from directly parsing and interpreting schema.org markup in the same way a search engine’s indexing system does. 

But AI-powered search features from Google (like AI Overviews) and Microsoft (Copilot) do not re-crawl the web independently for every query. Instead, they leverage the vast, continuously updated indices of their respective search engines (Google Search Index, Bing Search Index). And structured data plays a crucial role in how today’s search engines understand, organize, and retrieve information from the web.

Copilot’s knowledge sources documentation, for example, indicates a direct reliance on the Bing search index:

“When turned on, Web Search triggers when a user’s question might benefit from information on the web. It searches all public websites indexed by Bing.”

And remember that Google Search Central documentation on appearing in AI features that we mentioned previously? It says:

“Technical requirements for appearing in AI features: To be eligible to be shown as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to be shown in Google Search with a snippet, fulfilling the Search technical requirements. There are no additional technical requirements.”

If structured data helps enable traditional SERP snippets and some LLMs and AI search features rely on traditional search engine indices for up-todate information, then having proper structured data in place may also help your content appear in these AI-powered search features.

In short, while AI itself may not “read” structured data in a semantic way during its core training, structured data is vital for how normal search engines today ingest, understand, and organize information. Since AI platforms and AI SERP features rely on these organized search indices to retrieve the most current information, robust structured data implementation on your website may have a knock-on effect of improving the chances of your content being considered relevant and chosen as a source for AI-generated responses.

On Structured Data & Knowledge Graph Integration:

“Structured data is no longer only about getting rich results within Google’s search results. It’s the foundation for machines to make sense of their training data. As language models keep getting smarter, the winners are going to be the brands that treat their website’s content like data: interconnected, contextual, and queryable.

Schema markup, linked-up entities, and keeping your metadata consistent will aid LLMs in interpreting meaning rather than just words on a page.”

— Jonathan Clark, Managing Partner at Moving Traffic Media

A further consideration on GEO/AEO and structured data comes from Jarno van Driel’s interview on the Search With Candour podcast.

When asked, “With the growth of … natural language processing … do you think that shifts how important structured data is going forward?”, van Driel responded:

“The question is, is it financially viable? … The big advantage of structured data — any structured data set, it doesn’t necessarily have to be JSON-LD — the big advantage of those is that they really require little compute to be turned into something useful. And the biggest issue with all those LLMs, even the next generation five or ten years from now, is that it will cost a lot more compute to analyze unstructured content than to digest a piece of structured content. 

So even if meaning or understanding are not involved, it’s just a matter of money. It’s cheaper for any party to deal with structured data than to turn unstructured data into structured data to be able to use it. So that’s really the biggest benefit; it’s just a cost-efficient way for those parties to do something with data. Knowledge graphs and those kinds of repositories wouldn’t function without those large [structured] datasets.”

Further learning: strategies to build your brand’s AI visibility

Here, we’ve covered the essentials of Technical GEO. In Lumar’s comprehensive GEO / AEO Strategy eBook, “Your AI Search Playbook,” you can learn tactics to cover the other 3 pillars of a strong AI search strategy, including:

• Content GEO
• Entity GEO
• Brand Authority GEO

Get your free copy of our full 80-page guide to GEO here.

GEO / AEO Tools

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The post Technical Essentials for GEO / AEO – Is Your Website AI-Citable? appeared first on Lumar.

Each month, Lumar’s in-house tech SEO experts hand-pick some of the SEO industry’s top news stories from across the web to keep you up-to-date on all things SEO and website optimization.

For our April 2026 SEO & GEO/AEO news roundup, the top headlines include:  

• Google AI Mode is coming to Chrome’s address bar — users will soon be able to query AI Mode directly from the browser, with responses able to reference open tabs, images, and files.
• Google’s March 2026 core update is complete — specialist, niche sites continued to outperform content aggregators, extending a familiar trend in recent updates.
• Google bans back button hijacking — sites that use redirect tricks to prevent users from returning to previous pages will face manual spam actions or automated demotions starting June 15th, 2026.
• Google Search is evolving into a task engine — new AI Mode features let users track hotel prices, build full trip itineraries, and even have Google AI call stores to check stock.
• Cloudflare redirects AI training bots to canonical URLs — a new feature automatically turns canonical tags into 301 redirects for crawlers like GPTBot and ClaudeBot.
• AI traffic to US retail sites is surging — but most aren’t optimized for it — Adobe data shows high-intent AI-referred shoppers arriving at e-commerce sites that aren’t yet machine-readable.
• OpenAI ends its exclusivity deal with Microsoft — OpenAI can now pursue cloud deals with rivals like Amazon and Google, while Microsoft retains a non-exclusive license through 2032.
• OpenAI launches GPT-5.5, its most agentic model yet — with stronger multi-step reasoning and tool use, pushing ChatGPT further toward a closed-loop “super app” model.
• Anthropic released Claude Opus 4.7 with improved multi-step reasoning and multimodal capabilities.
• EU weighs classifying ChatGPT search as a “Very Large Online Search Engine” — at 159 million monthly EU users, it clears the 45 million threshold that would trigger DSA compliance obligations.
• (and more, below!)

Dive into the details behind the SEO and GEO/AEO headlines this month below…

But first: New AI visibility tools launched in Lumar this month!

Know where you stand in AI answers. New AI Visibility tools in Lumar track your brand’s AI presence, sentiment, and citations across the AI models your buyers are using — and tell you exactly what to do about the gaps.

Get a Demo of AI Visibility Tracking in Lumar

April 2026 SEO & GEO/AEO News Roundup:

Google announces greater integration for AI Mode in Chrome 

Google has been continuing its mission to better integrate AI Mode with everyday workflows. Soon, AI Mode will be available directly within Chrome via the address bar, allowing users to ask complex questions while staying on a page. AI Mode will also be able to reference open tabs, images, and files for extra context.

Clicking on links from these AI responses will also now open a side-by-side view, rather than replacing the current page. The goal is to reduce so-called ‘tab hopping’ and allow users to deep-dive into specific topics from one static location.

(Source: Google’s The Keyword Blog)

Google’s March 2026 core update completes its rollout

The update ran from March 27th to April 8th. There was no specific focus to this one, although SERP volatility was high. Much of the analysis that followed, including this article by Aleyda Solis, showed specialist, targeted sites performing better than content aggregators and comparison sites. It’s a familiar pattern that’s become a bit of a trend in recent updates, rewarding established brands that have built a strong reputation within their niche.

(Source: Google Status Dashboard)

Google makes recommendations for ‘Read more’ link inclusion

Google has published new guidance on optimizing content for ‘Read more’ link inclusion. These links have been cropping up more frequently in SERPs in recent months, but this is the first time Google has made any actionable recommendations to improve eligibility for the feature.

Recommendations focus on ensuring that content is accessible and directly visible on page load. Content hidden behind tabs or other expandable sections is less likely to appear here.

(Source: Search Engine Land)

‘Back button hijacking’ is about to be penalized by Google

Google has updated its spam policies to explicitly ban back button hijacking. This covers any behavior that stops users from returning to the previous page when they click the back button within the browser, such as redirecting them to a page they haven’t visited before.

Possible penalties include manual spam actions or automated demotions, which can impact the site’s performance in Google Search results. The new rules come into place from June 15th and sites have until then to disable this behavior.

(Source: Google Developers Blog)

Cloudflare turns canonical tags into redirects for AI training bots

Cloudflare has launched a new feature that automatically turns canonical tags into 301 redirects for certain AI training bots, such as GPTBot and ClaudeBot. The aim is to prevent AI crawlers from ingesting so much duplicate content, instead redirecting them straight to the primary version of each page.

The change will only impact AI training bots, so AI assistants will be unaffected. The change also won’t have any impact on Googlebot or human users. Even so, it brings canonical strategy into focus and is yet another way that AI crawlers will be acting differently from traditional search engines.

(Source: Cloudflare)

Google says efficiency is key when it comes to page weight concerns

In a recent episode of the Search Off The Record podcast, Gary Ilyes and Martin Splitt highlighted a growing discussion around increasing page sizes. The pair confirmed that larger pages aren’t necessarily a problem for Google. However, they do point to whether elements making up page weight are actually beneficial to users.

Bigger pages tend to rely heavily on things like JavaScript, images and structured data. Rather than looking at the size of the page in silo, it’s important to consider whether these elements are getting in the way of efficiency – both for crawlers and users. 

(Source: Search Engine Journal)

Google demystifies Googlebot and the 2MB fetch limit

In a recent blog post, Google has provided a rare glimpse into the inner workings of Googlebot. No longer referring to a single crawler, Googlebot has evolved into an umbrella term that covers dozens of different clients, all working to process content for use across Google’s wide range of products and platforms.

The blog post also references the 2MB limit, which sent shockwaves through the SEO industry when it was first mentioned back in February. Googlebot currently fetches up to the first 2MB of each individual URL. If your HTML file is larger than this, fetching will stop exactly at the 2MB cutoff. Each referenced resource in the HTML will have its own separate byte count and will not count towards the size of the parent page they were found on.

(Source: Google Developers Blog)

AI traffic surges for US retailers, but many sites are still not optimized for AI bots

A recent report from Adobe confirms that AI sources are sending rapidly growing volumes of high-intent traffic to U.S. retail sites, with shoppers using chatbots and AI browsers to research products and find the best deals. However, many e-commerce pages are still not fully optimized for AI search, especially when it comes to PDPs. This limits visibility in AI-powered searches and greatly reduces retailers’ ability to be cited within these AI shopping experiences.

(Source: Adobe)

The Ultimate Guide to GEO

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Google Search is evolving into a task engine: AI travel-planning and AI calling features expand in search

Google rolled out a cluster of new features in April that let users complete real-world tasks without leaving Search. This month, Google launched hotel price tracking globally, letting users monitor individual properties and receive email alerts when rates change — directly from the search bar. Alongside that, AI Mode’s Canvas tool now builds full trip itineraries for all US users from a natural language prompt, pulling together flights, hotels, mapped attractions, and refining results through follow-up questions. And AI Mode gained agentic calling — the ability for Google’s AI to phone local stores, check whether a specific item is in stock, and report back — extending a capability that has been available in Search since November.

Taken individually, these are useful travel features. Taken together, they represent a meaningful step toward a more agentic version of Google Search; one where users don’t find information and then go do things, but where Search handles the doing directly. 

For SEOs and digital marketers, agentic features in Search will reward businesses whose data is accurate, structured, and machine-queryable, not just findable by humans. If AI Mode is calling your store to check stock, your Google Business Profile and inventory data are now effectively front-line marketing assets. Schema.org markup, consistent local listings, and real-time operational accuracy matter in ways that go well beyond traditional ranking signals. The window to get ahead of this is now, before agentic search is the default expectation rather than the notable new feature.(Source: Search Engine Journal ; Travel + Leisure ; Google’s The Keyword Blog)

OpenAI ends exclusivity deal with Microsoft

This week, OpenAI and Microsoft have restructured their long-standing partnership. The headline change: Microsoft has dropped its exclusive right to sell OpenAI’s AI models, opening the door for OpenAI to pursue cloud deals with rivals like Amazon or Google.

This is a mutual unwinding of exclusivity rather than a total breakup of the partnership. Microsoft retains a non-exclusive license to OpenAI’s models through 2032, but trades away exclusive distribution rights in exchange for no longer having to pay OpenAI a revenue share. OpenAI gains a cap on its payments to Microsoft and the freedom to operate across multiple cloud providers, which can help it expand its enterprise AI offerings.

(Sources: Forbes ; Reuters )

OpenAI launches GPT-5.5, enabling more agentic task handling in ChatGPT 

This month, OpenAI has launched GPT 5.5, describing it as its “smartest and most intuitive” model to date and a key step toward a unified AI experience that brings search, productivity, and task execution into a single interface. The new GPT 5.5 model introduces stronger multi-step reasoning, improved tool use, and more agent-like behavior — enabling users to complete complex tasks without leaving the ChatGPT environment. 

Per OpenAI’s announcement post:

“GPT‑5.5 understands what you’re trying to do faster and can carry more of the work itself. It excels at writing and debugging code, researching online, analyzing data, creating documents and spreadsheets, operating software, and moving across tools until a task is finished.”

GPT 5.5 is rolling out to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex, with plans to bring the model to the API soon.  

For SEO and digital marketers, the bigger story is strategic. OpenAI is moving toward a closed-loop “super app” model, where users search, evaluate, and act all within the AI platform rather than clicking out to websites. This raises the stakes for achieving AI brand visibility, as brands increasingly compete to be cited or surfaced in AI responses, not just indexed and ranked in search engines.

(Sources: TechCrunch ; OpenAI announcement) 

Anthropic launches Claude Opus 4.7 with improvements in instruction-following and multimodal understanding

Like OpenAI, Anthropic also shipped a model update this month with the launch of Claude Opus 4.7. This latest Anthropic model is focused on improving performance in complex, multi-step tasks. Anthropic highlights that Opus 4.7 offers better instruction-following, greater ability to remember important notes across multiple sessions, and improved multimodal understanding. 

(Sources: Anthropic announcement ) 

 EU moves to classify ChatGPT as a “Very Large Online Search Engine”

The European Commission is assessing whether to designate ChatGPT’s search feature as a Very Large Online Search Engine (VLOSE) under the Digital Services Act, which would require stronger compliance with EU digital platform regulations. OpenAI’s transparency report revealed ChatGPT’s search feature averaged 159.1 million monthly active users in the EU — well above the 45 million threshold that triggers the classification. 

Per OpenAI documentation:

“For the six-month period ending 31 March 2026, ChatGPT search had approximately 159.1 million average monthly active recipients in the European Union.”

A VLOSE designation would place ChatGPT alongside Google, Microsoft, Amazon, Meta, and X, thereby requiring systemic risk assessments, algorithmic transparency, independent audits, and obligations for researchers to access data. 

(Sources: Reuters ; TechRepublic)

Did you know you can now optimize for AI search and LLM brand mentions with Lumar?

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Entities for AI Search: How to Make Your Brand Recognizable to LLMs

Before a generative AI system can cite your content, recommend your product, or include your brand in an answer, it needs to understand one thing:

Who you are.

In AI systems, an entity is a clearly defined, uniquely identifiable thing or concept — such as a company, product, person, or idea — that can be distinguished from others and connected to related information when they are embedded in a vector space. LLMs and retrieval systems use entities (not just keywords) to organize knowledge, linking them to attributes, relationships, and contexts. For your brand to be surfaced in AI-generated answers, it needs to exist as a recognizable entity within this network of meaning.

That’s where Entity GEO comes in. This aspect of generative engine optimization (or GEO, also known as answer engine optimization, or AEO) focuses on ensuring AI systems recognize your brand as an entity in order to include it in generative responses to users’ prompts. Entity GEO is a foundational pillar of AI search optimization, moving beyond keyword matching to establish a verifiable brand identity for the machines.

Effective Entity GEO builds a consistent, accurate identity for your brand across the web (not just on your website). This includes maintaining consistent brand descriptions across platforms, using structured data such as Organization, Product, or Person schema, securing inclusion in trusted third-party databases and knowledge graphs, and publishing content that clearly connects your brand with the topics you want to be known for. These entity signals help AI systems reliably recognize your brand, associate it with the right areas of expertise, and reference it accurately in generated responses.

On Brand Entity-Building for GEO:

“The brands that train AI to recognize who they are — through consistent entity signals, schema, and thought-leadership — will own visibility in 2026.”

. . .

“AI doesn’t think in keywords; it thinks in entities and relationships. That means the old way of optimizing for rankings doesn’t guarantee you’ll show up in AI responses… My tip: stop chasing prompts and start tracking entity presence.”

— Aimee Jurenka, SEO Strategist @ RicketyRoo

In our 2026 AI Search Playbook, we discuss both Entity GEO and Brand Authority GEO. While Entity GEO and Brand Authority GEO are closely related, they serve two distinct functions when it comes to influencing generative AI systems. Understanding the difference is critical because AI must first recognize your brand as a distinct entity before it can evaluate whether or not that entity is authoritative and trustworthy enough to cite.

Think of it this way:

• Entity GEO answers “Who are you?”
• Brand Authority GEO answers “Should we trust you?”

Both entity recognition and establishing brand authority are required for consistent inclusion in AI-generated responses, but they operate at different layers of AI understanding. At the Entity GEO layer, AI systems are not yet judging whether your brand is good or authoritative. They are determining whether your brand:

• Exists as a distinct concept 
• Can be clearly identified and disambiguated
• Has stable, consistent facts associated with it

The Ultimate Guide to GEO

Get the full guide to GEO here

Learn more about the 4 primary types of GEO – including technical GEO, content GEO, entity GEO, and brand authority GEO — in our FREE 80-page strategy guide for AI search!

How AI uses entity signals

Entity signals help AI answer foundational questions about your brand and product or service offering, such as:

• Is this a real organization, product, or person? 
• Is this brand distinct from similarly named entities? 
• What does this brand do?

Entity GEO tactics

Entity GEO efforts center on identity consistency and factual corroboration, including:

• Ensure consistent, accurate brand descriptions across platforms (your website, social media profiles, third-party databases, press coverage).
• Use entity-building structured data on your website, for example: ‘Organization’, ‘Product’, ‘Person’ schema.
• Get your brand included in trusted third-party databases and resources (such as Wikipedia, G2, industry lists, etc.)
• Build topical authority through content to create a clear relationship between your brand and key topics related to your offering and industry.

“Optimizing content for a specific set of keywords is increasingly seen as outdated… Strategy will pivot towards topic clustering, leveraging structured data, internal linking, and entity-focused content to position brands as authorities on broader subjects.”

— Natalie Stubbs, Senior Technical SEO @ Lumar

Outcomes of strong Entity GEO

The goal of Entity GEO is to ensure AI systems can:

• Reliably recognize your brand 
• Correctly associate your brand with specific topics 
• Confidently reference your brand without confusion

“I see GEO / AEO as the next phase of my Topic Entity Strategy — shifting from optimizing for search engines to teaching AI systems how to understand your expertise.

• Traditional entities (like people, places, or brands) tell AI who you are.
• Topic entities tell AI what you’re known for.

By mapping your brand to the core concepts you want AI to associate you with, like “local SEO”, “AI visibility”, or “semantic optimization”, you create a retrievable web of meaning.

This approach moves GEO / AEO beyond theory: instead of tracking prompts or keywords,

I track topic entity presence; how often your expertise shows up in AI answers, how well it’s understood, and how consistently it’s connected back to your brand.

That’s how we measure, optimize, and benchmark visibility in the age of retrieval.”

— Aimee Jurenka, SEO Strategist @ RicketyRoo

Entity-building and the GEO/AEO funnel

We’ve previously introduced the ‘GEO Funnel’ concept as a way of understanding the stages of generative engine optimization:

1. AI Discovery: First, AI needs to be able to discover and retrieve your content.  
2. AI Understanding: Then, AI needs to ‘understand’ your content and who you are. 
3. AI Selection & Inclusion: Finally, AI systems need to ‘judge’ and evaluate your content and your brand against others’ to determine which is the most trustworthy source, the best content to cite, or the best brand to mention in response to a user’s particular query. 

Entity GEO focuses on making sure your brand is recognized as a unique entity by AI systems. This aligns with the ‘AI Understanding’ stage in the GEO Funnel. 

Tools you need for AI visibility

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