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

As you’ve read in the Generative Experience/Engine Optimization post, AI search engines are creating more search engine users. They love what the AI LLMs give them — fast information and insight, even above quality.

AI search engines are arriving with big impact on content developers and marketers. It’s not in the future, it’s here now and we must become masters at building web content that AI LLMs will spider and determine is most relevant to search users inquiries.

And yes, we’ll need to be even better at traditional search engine optimization, because it’s not going away. Google search will evolve as it has many times and offer a different experience than ChatGPT, Copilot or Gemini.

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 and may generate leads, revenues and if we do it right — loyal customers. As more studies reveal how AI LLMs determine which websites they’ll show in results, we’ll be able to craft more powerful SEO/Content strategies to give us the results we’re after. In this brief post, you get a good introduction while seeing how AI search and is different from traditional. This will help you understand the need for your content strategist to take more sophisticated approach to developing content to satisfy both Google search and the AI LLMs. It’s pretty heady stuff, especially with the competition being intense (e.g., big corporations).

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

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.

Whether it’s a big change for SEO and content strategy remains to be seen. However AI SEO is a thing. Google and OpenAI are on a quest to replace human judgment with their AI-driven algorithms. If we’re going to stay visible and draw customer traffic from them, we need to master generative engine optimization (GEO).

GEO is geared to optimizing a fuller information session for AI LLM users — an interactive in-depth experience.  Users don’t just ask either. The AI LLMs will create contexts and info that moves users into new knowledge areas and insights.  Regular search engines don’t have that power. AI LLMs are chatbots which means the conversation and satisfaction with the content presented is utmost. It’s more than a keyword search. The AI LLMs seek to understand the user’s intent and even their level of understanding.

You need better content specifically designed and developed for ChatGPT/Gemini (top two) which fulfills the full set of features they demand in content.  You’re serving up a set of documents that are right on topic, useful, organized, authoritative, and trustworthy — same as regular SEO and content strategy.

Designing Content Specifically for ChatGPT/Google Gemini

Separate from your SEO strategy (and supported by it) I would advise developing specialized content entirely for AI LLMs and this would resonate with their specific algorithms, based on specific user-intent characteristics. You need the best material to win on ChatGPT/Gemini.

Recent surveys show users trust the AI search engines so sites that are mentioned or cited enjoy a big credibility boost. And if you’re content is developed to present a great brand image and solid trustworthiness, they connect the dots quickly. Then you can move them into your nurturing phase.  AI SEO means getting visibility with a sophisticated content I mentioned in the GEO post.

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 and our content has to be able to fulfill that question and many more. WE go from keyword search to question research.

It’s keywords vs questions, and why we include all the right answers within our new content (Answer engine optimization! 😊).

If you read my other posts on Search Generative Experience and Generative Experience Optimization, you’re aware that LLMs are growing in use.  They’re also a little more selective of the content they spider and index, perhaps filtering out paid content better than Google’s organic indexing system. They look at content differently, as an answer to questions and as a user-experience.

While Gary Ilyes, search engineer at Google said recently, that AI LLMs don’t require a different type of SEO, it’s clear there are different user intents and the results delivered to users are different. There’s more conversation/info exchange in GPT sessions.  Yet, even if the differences are subtle, we should know what they are.  It’s typical of Google to try to tone down any type of SEO opportunity. Their priority is to thwart free traffic.  AI LLMs are more user intent-sensitive and your pillar content piece (the one that has a chance to appear) must resonate to their user’s interests and purposes. Yes, advanced SEO already does that, with multi-pronged, rich information sets for different types of searchers.

Where AI LLMs differ then would be in the specificity of the user’s questions. and the context of the topic. Regular SEO never got that specific even with long tail phrases. This search experience is much deeper.  We should strive to ensure we are covering the actual questions they’re asking (research).

My knowledge of Ai search engines is improving, so I hope you’ll bookmark this post for future insights as I improve this live document.

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. This is what makes them so powerful to skilled talent who have experience and techniques to draw the best information out of them.

This is because they get more input from users in the search prompt, whereas Google or Bing responds to a few keywords (rarely a question).  Google will create an AI overview for questions that ultimately leads the searcher over to AI mode or Gemini.

Unlike Google and Bing which rely on keyword and link signals, LLMs analyze the semantic meaning, context, and emotional or situational cues in a user’s query. This allows them to generate or surface responses that best fulfill the user’s true goal, whether that’s finding a quick answer, planning a trip, comparing options, or seeking reassurance.

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.

A new report by Muckrack on AI LLMs, they found that LLMs are better at using credible, unpaid resources, and that fresh content is prioritized. In the resources cited, they found high-domain authority sites are cited much more.

Google of course, is integrating AI and its famous ranking algorithm to provide the best of both search experiences. This is SGE, or the search generative experience where Generative AI is producing personalized research and content for users (e.g., AI Overview, and AI mode).

Optimizing for Google and Bing’s AI overviews is important, but for the future, we need a solid strategy to position our content in these engines for a rising set of users. From real estate, to travel to shopping, they will find new ways to fulfill’s users desires and questions.

To summarize:

•   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

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:

• 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.

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

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