Why "Keywords" Don’t Matter in LLM Optimisation

For years, SEO has trained us to think like this:

• Start with a keyword list
• Analyse search volume and difficulty
• Produce content that “targets” each keyword
• Sprinkle variations into headings, subheads and body copy

Do that well, and Google will usually reward you. That mindset is so ingrained that when people talk about “optimising for AI” or “LLM optimisation”, they often reach for the same tools:

• “What prompts are people using?”
• “Which AI keywords should we target?”
• “Should we add ‘ChatGPT’ into our content so it ranks there too?”

Here’s the uncomfortable truth:

For large language models (LLMs), keywords are almost irrelevant in the way SEOs understand them.

That doesn’t mean language doesn’t matter. It does – a lot. But the game has changed from “exact match phrases” to intent, context and usefulness.

If you’re still thinking in keyword lists, you’re optimising for the wrong era.

How LLMs actually “see” your content

Let’s simplify how an LLM like GPT, Gemini or Claude works when it generates an answer:

1. It turns the user’s question into tokens (pieces of words), builds a representation of what that question means, and looks for the best way to continue the text.
2. It draws on its training to understand patterns of language, concepts and relationships – not just matching identical words.
3. If it’s using retrieval (e.g. browsing, plugins, RAG), it pulls in content that is semantically related to the question, not just content with the exact same keywords.
4. It blends that information into a coherent answer, guided by intent (“explain”, “compare”, “recommend”) and context.

Crucially:

• LLMs don’t need “best customer success platform for SaaS” in your H1 to recognise that a page about “customer retention and health-scoring for subscription businesses” is relevant.
• They can handle synonyms, paraphrases and messy, natural language questions far better than traditional keyword match systems.

They’re not counting keyword frequency. They’re trying to understand.

Why keyword thinking breaks down in the LLM world

Traditional keyword-based thinking falls apart with LLMs for a few reasons.

1. Buyers don’t ask neat, keyword-style questions

In the real world, no one types “enterprise risk analytics platform UK” into ChatGPT.

They ask things like:

• “We’re a UK bank. What’s the best way to build a risk analytics capability without a huge data science team?”
• “Tools that help a mid-sized bank move from spreadsheet-based risk reporting to something more automated?”

If your content is built around rigid keyword phrases, it may not actually address the messy, contextual questions buyers bring to LLMs.

LLMs don’t care whether you used the phrase “risk analytics platform” three times. They care whether you’ve explained and evidenced how you help banks automate risk reporting.

2. Synonyms and paraphrases are trivial for LLMs

For search engines, it used to matter whether you said:

• “customer success software”
• “customer success platform”
• “customer success tools”

For LLMs, that distinction is much less important. They’ve seen all of those phrases in similar contexts and can infer they’re related.

What does matter is whether your content:

• Clearly situates you in the right category
• Defines who you’re for and what jobs you do
• Provides enough detail for the model to use you as a credible example in an answer.

In other words, semantics and specificity trump keyword variants.

3. Intent matters more than exact phrases

LLMs are optimised to follow intent:

• “Explain X”
• “Compare A and B”
• “Recommend options for scenario Y”
• “Give me step-by-step guidance to do Z”

If your content is a thin, keyword-stuffed overview that never actually helps anyone, the model has no reason to lean on it. It will favour guides, comparisons, case studies, and implementation notes.

You can repeat the keyword all you like. If you never really answer the question, you won’t earn a place in the answer.

So what does matter for LLM optimisation?

If keywords aren’t the lever, what is? Three big things:

1. Real buyer intent
2. Structured, evidence-rich content
3. Clear signals about who you help and how

1. Start from buyer intent, not keyword lists

Instead of starting with “SEO keywords”, start with:

• ICP – who are we actually selling to?
• Needs / jobs-to-be-done – what are they trying to achieve?
• Research journey – what do they ask at each stage?

This is a much more useful starting point for LLM optimisation than “incident management software” or “risk ops tool” as abstract keywords.

2. Write content that really answers those questions

Once you have real buyer questions, the next step is to create content that would genuinely help if the LLM read it.

That means:

• Clear explanations, not buzzword soup
• Specific scenarios (“for mid-market insurers”, “for banks with multiple committees…”)
• Concrete steps and frameworks
• Evidence: numbers, examples, screenshots/workflows, case studies

“If I gave this page to a smart, sceptical human, would they walk away with a clearer understanding and a next step?”

If the answer is yes, you’re on the right track. If the answer is “well, we’ve ticked the keyword box”… you’re probably not.

3. Make it easy for LLMs to interpret and cite you

LLMs are more likely to use content that is:

• Accessible: Not locked behind forms or buried in unstructured PDFs
• Well structured: Clear headings, logical sections, markup that hints at what’s important
• Explicit about who and what: “We help [ICP] do [job] in [context]” – no ambiguity about the category you’re in
• Consistent: Same terminology across pages, no confusing mix of old and new positioning.

You don’t have to “stuff keywords” – but you do have to make meaning obvious.

From keyword lists to question maps

A practical way to shift your thinking is to replace Keyword lists with Question maps.

This mindset is much closer to how LLMs operate: they see questions in context, not as disembodied keyword stubs.

Measuring success without keywords

If you’re not chasing keyword rankings, how do you know if your LLM optimisation efforts are working? You look at:

• Prompt-level visibility: For a defined set of buyer questions, how often are you mentioned or recommended?
• Share of answer: When you are mentioned, are you buried in a long list or positioned as a leading option?
• Use of your evidence: Do AI assistants cite or link to your site? Do they reuse your frameworks, language or examples?
• Changes over time: If you improve content for a specific theme or ICP, does your presence in answers change in the next cycle?

That’s the sort of measurement we built Odyssiant around: not “did we use the keyword?”, but:

“When buyers ask AI assistants the questions that matter, how often do we win the answer – and why?”

The mindset shift: from stuffing to serving

If you remember nothing else, remember this:

LLMs don’t need you to stuff keywords. They need you to serve buyers.

Stop asking: “Have we hit the keyword three times in H1/H2/body?”

Start asking: “If our ideal buyer pasted this question into ChatGPT, is our content the one answer we’d be proud to see appear?”

If the answer is yes, you’re doing LLM optimisation already – you’re just calling it something else: useful, focused, buyer-led content.

Tools like Odyssiant can help you see, at scale, whether that content is actually changing what AI says about you. But the underlying shift is human:

• From chasing keywords
• To understanding questions
• And earning the right to be the answer.