How to Optimize for AI Search: The Shift from Keywords to Context

There’s no “ranking” in LLMs the way there is in Google. No search results, no page two. Because of that, it’s worth remembering the real purpose of your content.

Here are a few examples:

1. Be an authoritative voice to be referenced (like a media company)
2. Answer specific user questions (e.g., “how to grill a quesadilla”)
3. Provide solutions to problems (“I’m traveling to Japan for the first time, what should I do?”)
4. Meet needs (“I need to get my dad a birthday gift”)
5. Support practical decisions (“I want a Justin Jefferson jersey”)

The goal of any GPT model is to give relevant, helpful information through conversation. If your website is packed with keyword stuffing and low-value filler, it won’t feel relevant to users and the model will stop recommending you altogether.

Black-hat tactics like keyword stuffing can backfire hard. LLMs adapt based on user feedback, and users give that feedback in real time, often brutally. If your brand is seen as misleading, you’ll train the model against yourself. (In theory, there’s no public proof yet but it’s logical given how reinforcement works.)

Content vs. Context in LLM SEO

Traditional SEO targets keywords. You’re optimizing for the words someone types.

LLM SEO targets context. You’re optimizing for how well your content satisfies what the user means, not just what they say.

Example:

A user searches, “Best shorts for Disneyland.” In traditional SEO, every site optimized for that keyword competes for the click.

But if a user asks an LLM:

“I’m visiting Disneyland in August. Give me an itinerary and recommendations for first-time visitors.”

Now the model understands the context - a first-time visitor going in summer. It may infer they’ll want lightweight clothing. If your store clearly describes fabric, fit, and climate use, you might get recommended without ever targeting “best Disneyland shorts.”

That’s context in action.

Why Context Beats Keywords

Content lives in silos, top of funnel blogs, bottom funnel product pages because it’s built around keywords.

Context connects the entire journey.

LLMs infer relevance from data. Continuing the shorts example:

If a user dislikes polyester and you don’t specify materials, the model must guess. If it can’t infer the answer, you’re out of the running. If it can infer it, you might show up. But if it knows because your data is explicit, you’ve met a key eligibility requirement.

Then come the trust signals:

• Are you active on social?
• Verified on directories (Google, Bing, Yelp)?
• Do reviews describe real problem-solving outcomes?
• Are there external sites validating your claims?

These factors influence whether an LLM trusts your data enough to reference it.

Why This Matters

You can’t rely on “doing SEO best practices” anymore. If your competitors also list FAQs and optimized meta tags, there’s no unique reason for a model to choose you.

LLMs prioritize relationships between concepts, entities, and trustworthy signals, not keyword density.

The Takeaway

LLM SEO isn’t about ranking for a keyword, it’s about being understood and trusted in context.

It forces better site structure, better data, and a better user experience.

The good news: what’s good for AI is also good for humans.

Follow along for upcoming posts on how to leverage LLM SEO like a pro.