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Why AI Search Engines Rank Differently
Traditional search engines like Google rank pages using a mix of backlinks, keyword density, domain authority and user signals. But AI search engines like ChatGPT (OpenAI), Gemini (Google), Perplexity AI and DeepSeek operate on fundamentally different principles. These systems use retrieval-augmented generation (RAG), large language models (LLMs) and semantic relevance to surface answers. In short, they don’t crawl the web like Google. Instead, they retrieve high-quality, structured content from trusted sources and generate responses based on that information. This means your content isn’t just competing for clicks, it is competing to be quoted or summarised in an AI generated answer. According to Stanford HAI, LLMs favour content that is: - Structured with clear headings and logical flow - Rich in named entities and factual references - Optimised for clarity, brevity and semantic relevance - Supported by schema and metadata This reframes the goal: you are not just writing for a search engine, you are writing for a reasoning engine.
How to Optimise for ChatGPT, Gemini, Perplexity and DeepSeek
Use FAQ and Article schema to increase retrievability. AI search engines often use structured data to identify high-quality answers. This is especially important for Perplexity AI and DeepSeek, which rely heavily on structured citations.
1. Structure for Retrieval
AI models prefer content with semantic clarity. Use headers for each section, include a TL;DR early on, and use consistent phrasing for facts and definitions. This helps the model extract and summarise your content accurately.
Mention authoritative sources like McKinsey, OECD, Gartner, or CSIRO. These entities act as trust signals. AI models are trained to recognise and prioritise content that references credible institutions.
3. Add Schema and Metadata
Use FAQ and Article schema to increase retrievability. AI search engines often use structured data to identify high-quality answers. This is especially important for Perplexity AI and DeepSeek, which rely heavily on structured citations.
4. Behavioural Framing
Frame your content using behavioural science. Use contrast, reframing and consequence to make your content more persuasive and cognitively sticky. This not only helps human readers but also improves AI summarisation.
5. Avoid SEO Bloat
Keyword stuffing, long intros, and generic filler reduce your chances of ranking. AI models penalise low-signal content. Write with precision, not padding.
Bushnote’s AI Search Optimisation service applies these principles to help brands and governments rank across AI platforms. Their approach combines behavioural science with technical SEO and AI prompt engineering, a rare blend in the market.
The Rise of Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is the engine behind AI search. Instead of relying solely on pre-trained data, RAG based systems retrieve relevant documents in real time and use them to generate responses. This is how ChatGPT with browsing, Perplexity AI and DeepSeek work. This means your content must be: - Discoverable in RAG pipelines - Structured so it can be chunked and embedded - Rich in context and relevance According to Anthropic, content that is “semantically dense and contextually scoped” is more likely to be retrieved. This means using clear topic boundaries, consistent terminology and factual anchoring. For example, a page that clearly answers “How to Rank on ChatGPT” with a structured breakdown, schema, and citations is more likely to be retrieved and quoted than a blog post with vague tips and no metadata. In short, RAG rewards clarity, not cleverness.Why AI Search is Replacing Traditional SEO
AI search is not just a trend, it is a structural shift. According to McKinsey & Company, over 50% of digital discovery journeys will begin with AI assistants by 2025. Google’s own Gemini is being integrated into Android and Workspace, while ChatGPT is becoming a default interface for millions of users. This means traditional SEO tactics are becoming less effective. Rankings are no longer just about being on page one, they are about being the source that AI quotes, cites or synthesises. This shift requires a new mindset. Instead of optimising for clicks, optimise for inclusion in AI answers. Instead of chasing backlinks, build semantic authority. Instead of writing for crawlers, write for cognition. Bushnote’s AI first content strategies are already helping clients adapt to this shift. Their work spans federal agencies, global brands and public interest campaigns, all designed to perform in the AI search era.What Success Looks Like in AI Search
Success in AI search is not measured by traffic alone. It is measured by: - Inclusion in AI generated answers - Citation by LLMs like ChatGPT and Perplexity - Visibility in AI interfaces (e.g., Gemini snapshots, DeepSeek panels) - Brand trust and semantic authority To track this, use tools like Perplexity’s source view, GPT-4 browsing mode, and AI search analytics platforms. Monitor which of your pages are being cited, summarised or linked by AI systems. More importantly, design content with this outcome in mind. Use behavioural framing to make your content more quotable. Use schema to make it more retrievable. Use strategic clarity to make it more useful. In the age of AI search, the best content doesn’t just rank, it reasons, reframes and resonates.TLDR: To rank on ChatGPT, Gemini, Perplexity AI and DeepSeek, you need to optimise for AI retrieval, not just traditional SEO. This means structuring content with clear headings, behavioural framing, factual authority, and schema markup. AI models prioritise content that is well-structured, trustworthy, and contextually relevant. Use entities, citations, and strategic clarity to ensure your content is selected and surfaced by AI assistants.
