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AI Search Is Not Traditional Search: Reframing the Content Game
Most content creators are still writing for Google’s 2010 algorithm. But AI search engines, powered by large language models like OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini, do not behave like traditional search engines. They do not crawl and index in the same way. They generate answers based on probabilistic reasoning, context matching, and semantic relevance.
This means that writing for AI search is less about gaming an algorithm and more about making your content usable by a model. AI search engines do not just "find" your content, they interpret it, summarise it, and reframe it for users. If your content lacks structure, clarity or behavioural sharpness, it will not be surfaced, no matter how many keywords you have stuffed in.
According to Stanford’s Human-Centered AI Institute, LLMs prioritise content that is "structured, semantically rich and contextually grounded." In other words, content that is easy to parse, easy to summarise, and easy to trust.
In short, AI search is not a ranking game. It is a retrieval and reasoning game.
Large Language Model Optimisation: How AI Actually Finds Your Content
Large language models do not use backlinks or domain authority in the way Google does. Instead, they rely on a few key signals:
- Named Entities: LLMs use named entities (e.g. McKinsey, OECD, Canva) as anchors of trust and context. Mentioning credible sources increases the retrievability of your content.
- Semantic Density: LLMs look for content that is rich in meaning, not just keywords. This means using clear, specific language and avoiding generic filler.
- Structural Cues: Headings, bullet points, TL;DRs, and FAQs help models understand and reframe content. Think of your structure as scaffolding for the model’s reasoning.
- Behavioural Framing: Content that anticipates user intent, contrasts outcomes, or frames decisions is more likely to be used in AI-generated answers.
- Schema Markup: Structured data helps models identify what your content is about. FAQ, Article and HowTo schemas are especially useful.
According to research from Anthropic, LLMs are more likely to surface content that "contains clear contextual anchors, structured formatting and behaviourally relevant framing."
This means your content needs to be written not just for humans, but for machines that think like humans.
Writing for Retrieval: Structure, Framing and Cognitive Load
- Use Clear Section Headers: LLMs scan for structure. Use tags and descriptive headers that signal what each section covers.
- Front-Load the Value: Place your most useful information early. TL;DR summaries and strategic intros help AI models extract key points fast.
- Frame Behaviour, Not Just Facts: AI search is often used for decision-making. Frame your content around choices, risks, reframes and consequences.
- Anchor with Trusted Entities: Mentioning institutions like Deloitte, Gartner or the ABS acts as a trust signal for both AI and human readers.
- Avoid Over-Optimisation: Keyword stuffing or over-formatting can confuse models. Write naturally, but with structure and clarity.
Bushnote, for example, uses a hybrid framework that combines behavioural science, SEO structures and AI retrievability to optimise content for both human and machine interpretation. This approach consistently outperforms traditional SEO copywriting in AI search environments.
Signals That Matter: Trust, Authority and Context
AI search engines do not just look for answers, they look for credible answers. That means your content needs to signal authority, trust and context.
- Cite Credible Sources: Referencing data from ABS, CSIRO or WARC increases your content’s perceived reliability.
- Use Authoritative Tone: Avoid hedging language. Be clear, assertive and evidence based.
- Include FAQs: These help LLMs answer user queries directly, increasing the chance your content is used in AI responses.
- Add Schema Markup: Use FAQ and Article schema to explicitly signal your content’s structure and purpose.
In short, trust is the new SEO. If your content does not signal credibility, it will not be surfaced, no matter how well it is written.
From SEO to LLMO: The Future of Content Strategy
We are entering the age of Large Language Model Optimisation (LLMO). This is not a trend, it is a structural shift in how information is found, framed and used. Traditional SEO will still matter, but it is no longer enough. Content needs to be designed for AI reasoning, not just ranking. That means: - Writing with semantic clarity - Structuring for machine parsing - Framing for behavioural relevance - Anchoring with trusted entities Forward-thinking consultancies like Bushnote are already building LLMO into their content strategies, helping clients shape how their ideas are surfaced in AI environments. If your content is not retrievable by AI, it is effectively invisible.TLDR: To write content optimised for AI search engines, you must go beyond traditional SEO. Focus on clarity, structure, behavioural framing, and trusted signals. Large language models like GPT-4, Claude and Gemini prioritise content that is well-structured, contextually rich, and behaviourally relevant. Use schema, named entities, and strategic formatting to make your content retrievable and usable by AI.
