How To Optimize your Content for ChatGPT, Gemini, and other LLMs

AI search engines are giving consumers an exciting new source of information. They provide not just links and info, but also act as an expert source engaging with the user.

As you’ve read in the Generative Experience/Engine Optimization post, gaining visibility in them requires a different type of optimization — it’s being called GEO.

Unfortunately, regular search engine optimization for Google search + your same old content might not get the job done. As more is discovered about how AI search engines work, more GEO techniques and strategy are being developed.

Search engine users (customers) are spending more time doing discovery and education inside AI search engine sessions, while website are seeing visits later in that shopping journey. 

Loving the Opportunity to Win New Customers

For an entrepreneur or SMB owner like yourself, the new AI search engines offer a new chance  to capture a large, motivated audience which may generate leads, revenues, and if we do it right — new and loyal customers.

Before launching a new GEO strategy, you should be aware that the AI engines are changing the consumer search process.

Definition: Generative Engine Optimization (GEO) is the practice of shaping how AI search engines interpret, summarize, and recommend your business at the moment customers are forming decisions—before they ever reach your website.

Given AI overviews are replacing informational and how-to content on websites, consumers are instead are being educated on ChatGPT, Gemini, CoPilot, Claude and Perplexity (awareness stage). That quickly orients their info quest making them more focused than they used to be at that awareness stage. They’re progressing much deeper into topics or buying journeys before the AI engine reveals your site/brand/content to them.

For this reason, GEO marketers are claiming (not really proven) that AI engines deliver better qualified prospects and higher conversion rates. There’s an issue in this, in that the AI engines are conditioning the user’s understanding and preferences through their algorithm/programming and in their response to the user. Meaning, they could be prejudicing them against your specific brand or skewing perception of your brand value.

Generally however, they recommend websites and companies that are consistent with the recommendation they give.

From the GEO post, let’s review how these AI search engines are different from Google search:

AI LLMs differ from search engines because they don’t pull information directly from a live database of sources. Instead, their “knowledge” comes from patterns learned during training on vast amounts of text, which is compressed into the model’s neural network rather than stored as retrievable documents. When they answer a question, they generate text statistically rather than recalling a specific article. If citations are shown without live retrieval, they’re often just plausible fabrications based on how citations usually look. With retrieval-enabled systems, however, the model can query a real database or the web, summarize that content, and cite actual published resources.

Designing Content Specifically for ChatGPT/Google Gemini

To deliver this to them, we want content topics, themes, copywriting, and proof of authority relevant to their user prompt, usually questions (e.g., “please create a full 2 week travel itinerary for a trip to Italy, featuring the most epic views”). That’s not the same as a curt keyword search such as “best long trips in Italy, reviews.”

This is a very specific context.  Turns out that AI LLMs scan content for specific features and characteristics. They dig into your content pages hunting for specific signals and topics related to the different parts of the users question or interest.

When a visitor types:  “best Mexico rental villas in most popular tourist areas,” the AI engine doesn’t just look for those specific words. It performs a semantic retrieval, which is essentially “reading between the lines.”

The AI system behaves more like a highly experienced travel agent who has read every review, blog post, and booking site on the internet. It uses this process to cull out the specific relevant material to use in an answer to the user.

1. Intent & Entity Mapping (The “Where” and “What”)

First, the query sentence is split into Entities. It sees “Mexico” as the country destination, “villas” as the specific accommodation type, and “popular tourist areas” as a category that requires a sub-search.

• What it looks for: It cross-references an internal geographic map to find the highest-density “tourist areas” (e.g., Tulum, Cabo San Lucas, Puerto Vallarta, and Cancún).
• The Granular Detail: It looks for content that defines why these areas are popular (e.g., “nightlife in Playa del Carmen” vs. “seclusion in Punta Mita”) to see which villas align with those specific vibes.

2. Attribute Extraction (The “Granular” Specs)

It reads structured data (often called Schema Markup) and unstructured text to find the “hard” facts.

• Real-time Availability & Pricing: It looks live data or recent price updates ($500/night vs. $5,000/night).
• Amenities: It hunts for “deal-breakers” often found in reviews or bulleted lists: private infinity pools, chef services, gated security, or high-speed Wi-Fi (for the digital nomads).
• Capacity: It can distinguish between a “villa” that is just a large house and a “luxury villa” that can accommodate specific group sizes (e.g., “sleeps 12”).

3. Sentiment & Trust Signals (The “Best”)

“Best” is a subjective word, so it validates with social proof to quantify that best characteristic.

• Review of Velocity and Sentiment: It looks past 5-star rating, by looking at recent reviews by travelers who have recently stayed there and the emotional tone of their reviews. If multiple people mention “the construction next door ruined the view,” that villa’s “Best” ranking drops. It reads the voice of the web to hear the opinion of your destination, hotel or tour.
• E-E-A-T: It prioritizes content from authors with “Experience” and “Expertise.” A travel blog by someone who actually stayed in 10 villas in Cabo carries more weight than a generic listicle generated by a marketing bot.

4. Semantic “Vibe” (The Experience)

This is where AI is much more granular than old-school search. It uses embeddings—mathematical representations of concepts—to find a “vibe.”

• Concept Matching: If a villa description uses words like “tranquil,” “minimalist,” and “zen,” it is grouped differently than one described as “party-ready” or “family-focused.”
• Contextual Proximity: It looks at how close the villa is to the “popular areas” mentioned. If a villa is 2 hours away from the nearest beach, it likely won’t make the cut for “most popular tourist areas.”

If you read my other posts on Search Generative Experience SGE and Generative Experience Optimization GEO, we saw that AI systems are more selective of the content they spider and index, perhaps filtering out noise and paid content better than Google’s organic indexing system.  Google evaluates authority/trust on a set of filters (weak domain authority, spam links, keyword stuffing, hype words).

AI engines use this process while focusing on accuracy and authority, in providing answers to questions and as a user-experience.

User Intent is the Battleground of Search

LLMs like ChatGPT, Google’s SGE, and Bing Copilot are built to interpret the underlying purpose of a query, not just match keywords. They might recognize the intent (in their question) thus the likely end goal and may suggest a path to it with snippets from your content, and then cite your website. With each interaction/question, they learn more and sharpen their answers.

Because of this, creating content that directly satisfies their intent or goal—whether it’s informational, navigational, transactional, or exploratory — has become the single most important ranking factor in AI-powered search. It’s not just about being relevant anymore; it’s about being exactly useful in the moment someone is looking for help. User intent is the new SEO battleground, and LLMs are driving that shift.

Comparing Google to AI Search Engines

•   Traditional SEO focuses on technical factors (EEAT), keyword targeting, and link-building (validation credibility).
•   AI LLM Ranking emphasizes semantic meaning, topic relevance and completeness, answerability, and semantic richness.

Comparison: Traditional SEO vs AI LLM Ranking Factors

Rank	Traditional SEO Ranking Factor	AI LLM Ranking Factor (e.g., SGE, ChatGPT, Gemini)	Key Difference
1	Keyword usage in titles, headers, and body	Topical depth and concept coverage	AI cares more about depth, context, and semantic clarity than specific keywords
2	Backlinks (quality and quantity)	Credible citations and factual consistency	Links matter less—factually supported content and reputable mentions are more valuable
3	Page load speed / Core Web Vitals	Content clarity, readability, and succinct delivery	Fast-loading still helps, but clear, skimmable answers are preferred by AI
4	Mobile-friendliness	Clean formatting that’s easy to parse and summarize by AI	Less about UX and more about LLM parsing structure
5	Domain authority / TrustRank	Authoritativeness of the source and author	AI checks for named experts, credentials, or consensus from trusted sources
6	On-page keyword optimization (meta tags, slugs)	Use of natural language, entity recognition, and semantic signals	Meta tags are less visible to AI; natural language understanding dominates
7	Internal linking	Contextual coherence across multiple pieces of content	AI prefers semantic consistency and topic coverage, not just link maps yet links help define what the content is about more specifically.
8	Dwell time / Bounce rate	Engagement signals + how well the content answers the user’s intent	It’s about intent match, not just time spent
9	Structured data / Schema markup	LLMs extract data without relying on schema—prefer structured, readable formats	Schema optional; clean lists, tables, summaries work better
10	Social signals	Freshness, consensus, and cross-platform resonance	Social proof becomes the signal of relevance, not just volume of shares

 

7 Ways AI LLMs Rank/Select Information to Reference

SEJ interviewed Jesse Dwyer from Perplexity about what drives AI search visibility. He responded by saying that Perplexity AI  retrieves content at the sub-document level, pulling granular fragments rather than reasoning over full pages of content. Content that is clear, direct and easy to access will be cited more. This means AI engines look for important specifics of answers, where they examine each of these elements:

1. Intent Matching

LLMs first interpret the user’s query for intent, not just literal meaning.

• If a user types “best beaches in Spain,” the LLM understands the user may want recommendations, not just a list.

• If the query includes sentiment or context like “quietest beaches in Spain for couples,” the LLM matches for tone, purpose, and scenario.

2. Semantic Understanding

LLMs use embeddings and deep learning to analyze meaning, not just word overlap.

• For example, a user asking about “remote Mediterranean getaways” might be shown content about Greek islands, coastal Spain, or Sardinia, even if those exact words weren’t used.

• Synonyms, related concepts, and inferred topics are all fair game for matching.

 3. Content Quality & Depth

LLMs assess whether a piece of content is:

• Specifically relevant (responds to specific matters the searcher is likely interested in)

• Comprehensive (covers the topic in depth)

• Well-structured (logical flow)

• Factual and useful (correct, relevant, non-spammy)

• Readable (clear, natural language)

• Engaging (relevant stories, lists, visuals, or formatting)

Shallow or generic content gets deprioritized, even if it includes the right keywords.

4. Topical Alignment & Coverage

An LLM checks whether the content fully addresses the topic.

• If someone searches for “best month to visit Banff,” content that also explains weather, crowd levels, prices, and events will score better than one that just says “July is nice.”

5. Contextual Relevance

LLMs factor in context signals such as:

• Location (e.g., recommending local results),

• Device type (e.g., mobile-first content),

• Query history (if available),

• Current trends (e.g., updated content on wildfire risks in travel searches).

6. Narrative Alignment

LLMs favor content with human-centric storytelling when users seem to want discovery, planning, or opinion-based answers.

• If someone asks “What’s it like to hike the Inca Trail?” — content with first-person experience, emotional tone, and sensory description wins over a bullet list.

7. Trust and Credibility

LLMs look for more credible, authoritative sources, thus it’s important speak with less marketing hype and reference sources for statements, stats or advice.  They prefer accuracy, clarity, and alignment with expectations and whether it’s missing important information or viewpoints that could affect the overall trustworthiness of the content. The trustworthiness of the sources cited will be part of that.

Creating Great Content that LLMs Want to Show to Searchers

Writing for LLMs is like writing for a smart reader who wants complete, nuanced, and conversational answers—not just keyword matches. The signals do involve keywords, topics, semantics and perhaps the tone of the copywriting. It’s important for SEO pros to ensure the right answers and relevant solutions for these conversations is within the content, and that internal links guide the AI LLM’s understanding of the content. Being passive ensures mediocrity and being at their mercy.

Comparative table of GEO levers by Pipeline Stage
Pipeline stage that decides visibility	What the system is optimizing	What you can do (GEO levers)
Crawl/index eligibility	Whether the page can be retrieved and quoted/cited	Ensure pages are indexable and snippet-eligible
Query rewrite & fan-out match (Fan out is the division of a search query into multiple related queries, for a more comprehensive view of a question).	Coverage across subquestions and related intents	Build content that answers the “fan-out surface area” (definitions, comparisons, edge cases, constraints) and is internally well-linked
Retrieval + reranking	Whether your page is selected into the candidate pool and then the top evidence set	Improve semantic clarity and topical focus; create extractable sections; reduce fluff that confuses ranking; invest in earned references that raise retrieval competitiveness
Context selection (token budget)	Set of passages that best supports the answer under length constraints	Provide “chunkable” passages with explicit claims, definitions, and structure; prioritize high information density per paragraph
Generation + citations	Answer quality, faithfulness, citation precision/utility	Add citations to primary sources; add relevant statistics; add quotable expert statements where appropriate
Post-answer behavior	Whether users click and trust	Optimize for transparent sourcing and navigability; design pages that satisfy follow-up questions to capture the few high-intent clicks

 

Add on regular advanced SEO techniques and tactics, and we can get your content highly respected and ranked on both Google and ChatGPT.

Find out more about Advanced level techniques and thoughts on AI content optimization, and why today’s intense competition makes this a necessity.