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Content | Artificial Intelligence
Optimising content for LLMs is not the same as traditional...
By Vanshaj Sharma
Feb 17, 2026 | 5 Minutes | 
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The way content gets discovered has shifted. Not slowly, not subtly. Search behaviour is changing fast and a growing slice of that behaviour now runs through large language models. People ask ChatGPT questions they used to type into Google. They use Perplexity to research products. They let Claude summarise long documents before they decide whether to read them. If your content is not structured in a way that these models can understand, extract, reference, it does not matter how well it ranks in traditional search.
Optimising content for LLMs is not the same as SEO. It rhymes with it, borrows some of its logic, but it operates on different principles entirely.
Search engines crawl, index, rank. They are looking for signals: backlinks, keywords, page speed, structured data. LLMs do something different. They are trained on large bodies of text and later, some of them retrieve content in real time to answer queries. What both scenarios share is a preference for content that is clear, well structured, factually grounded and easy to parse at scale.
An LLM does not get distracted by flashy visuals or scroll animations. It reads what is written. So if your writing is vague, buried in jargon, or padded with filler, the model either skips it, misrepresents it, or fails to surface it altogether.
That is the core tension content teams need to reckon with.
The single biggest mistake content writers make when trying to optimise content for LLMs is assuming that clarity is just a writing style preference. It is not. It is a functional requirement.
LLMs process text by pattern matching against enormous training datasets. When a passage is clear, specific and logically structured, the model can accurately represent it. When it is ambiguous or over qualified, the model may hallucinate details or simply omit the passage in favour of something cleaner.
Practically, this means:
• State the main point at the start of every section, not at the end • Use concrete nouns and verbs rather than abstract concepts • Avoid passive voice where possible; models extract subject action object relationships more reliably from active constructions • Keep sentences purposeful; every sentence should be doing work
A paragraph that opens with a clearly stated claim, then supports it with specific detail, is significantly more likely to be accurately cited or surfaced by a language model than one that builds slowly toward a buried insight.
Heading hierarchy matters enormously. H1 for the main topic, H2 for major sections, H3 for subsections within those. It sounds basic because it is. But a surprising number of long form articles skip meaningful subheadings entirely, or use them decoratively without matching the content underneath.
For LLM optimisation, think of every H2 as a standalone answer to a likely question. If someone asks "how should I format content for AI models", a section titled "Use Consistent, Scannable Formatting" will get picked up more cleanly than one titled something vague like "Getting Things Right".
Short paragraphs help too. Not because readers prefer them (though many do) but because models handle dense, unbroken blocks of prose less efficiently. Breaking ideas into focused, self contained paragraphs makes each idea easier to extract and reference independently.
Generic content is the enemy of LLM visibility. Models are trained to prioritise content that contains verifiable, specific information over content that speaks in broad strokes.
"Many businesses have seen improvements" is not useful to a model. "A study by Stanford researchers in 2023 found that structured content reduced hallucination rates by 21% in retrieval augmented systems" is far more usable, more citable, more likely to survive the extraction process intact.
When writing content with LLM optimisation in mind, ask this question constantly: would a researcher find this passage useful? Would they be able to cite it? If the answer is no because the claims are too soft or too generalised, that is a signal to revise.
This does not mean every sentence needs a footnote. It means being specific about numbers, timeframes, processes and outcomes wherever possible.
People do not type full sentences into search bars. But they do ask full questions to LLMs. "What are the best practices to optimise content for LLMs?" is the kind of phrasing someone actually uses. Your content should anticipate these natural language patterns and address them directly.
This has two implications. First, question based subheadings perform well, both for featured snippets and for LLM retrieval. Second, the content under those subheadings should answer the question quickly, before elaborating.
The days of burying the answer deep in a 2,000 word article to maximise time on page are genuinely over for this context. LLMs do not care about dwell time. They want the answer up front.
LLMs build understanding through entities: people, places, products, concepts and the relationships between them. Content that clearly defines and consistently references relevant entities is easier for models to situate within a knowledge graph.
This means using the proper names of tools, frameworks and concepts rather than vague references. "AI writing tools" is weaker than "GPT 4 or Claude." "A popular productivity platform" is weaker than "Notion." Specificity signals expertise and it makes content more navigable for both models and readers.
Topical authority still matters too. A single well written article will not cut through the way a coherent content ecosystem will. If every piece you publish on a topic reinforces the same entities, addresses related questions and links to other credible sources, models are more likely to treat your content as an authoritative cluster on that topic.
Most LLM systems that retrieve real time content rely on some form of crawling or API based access. Schema markup, clean metadata and accurate Open Graph tags make it easier for these systems to understand what a page is about before processing the full text.
Article schema, FAQ schema and HowTo schema all serve a practical function here. They are not just for rich snippets in Google anymore. They signal structure and intent to any automated system parsing your content.
If your CMS supports structured data and you are not using it, that is low hanging fruit worth picking up immediately.
LLMs have training cutoffs, but retrieval augmented systems pull live content. Outdated information can damage your credibility within an LLM response if the model flags a contradiction between your content and more recent sources.
Regular content audits are not optional anymore. Dates, statistics, product details, regulatory information: all of it ages. A piece that was accurate 18 months ago may now be actively misleading. Keeping content fresh is a basic maintenance task that directly affects how models will represent your brand.
Nobody has this fully figured out. The field is moving quickly, the models are changing and what works today may need revisiting in six months. The best approach is to build content that serves human readers first, because models are increasingly good at recognising genuinely useful content versus content that is trying to game a system.
Clear writing. Specific claims. Logical structure. Real answers to real questions. These are not tricks. They are just good content fundamentals applied to a new distribution channel.
The brands that will be cited by AI systems in two years are the ones publishing content worth citing right now.