How to optimise your content for generative AI

You have published 50 blog articles, gained hundreds of backlinks... and yet ChatGPT never cites you. The problem is not your authority — it is your content format.

LLMs do not read like a human. They extract text fragments, evaluate them for relevance and reliability, then synthesise them into a response. A classic SEO article — optimised for Google's 10 blue links — is often unreadable for an LLM: too much padding, too few facts, vague structure.

GEO (Generative Engine Optimization) is the discipline that corrects this. This guide gives you the concrete rules, tested on 200+ articles for our clients at AISOS.

1. Why AI content differs from SEO content

Classic SEO optimises for a ranking algorithm that evaluates signals like backlinks, keyword density and time on page. Generative AI works radically differently:

• Fragment extraction: LLMs extract passages of 50-200 words, not entire pages. Each paragraph must be self-contained and informative.
• Reliability assessment: LLMs favour content with figures, cited sources and clear definitions.
• Multi-source synthesis: the LLM combines information from 5-10 sources. Your content must bring a unique piece of the puzzle, not a generic synthesis.
• Semantic understanding: LLMs understand meaning, not keywords. Information quality takes precedence over keyword density.

Dr. Sebastian Riedel, Director of the UCL NLP Lab (London), who has contributed to research on retrieval augmented generation, explains:

"Language models do not reward keyword stuffing — they reward informational precision. A well-structured 100-word factual paragraph will be more likely to be cited than a 3,000-word article filled with generalities."

2. The 8 optimisation rules for generative AI

Rule 1: start with a clear definition

Every article must contain a clear definition of the concept covered in the first 200 words. Recommended format: "[Concept] is [definition]. [Context]. [Importance]." LLMs use these definitions as the basis for their responses.

Rule 2: one fact per paragraph

Each paragraph should contain exactly one fact, statistic or claim. Paragraphs of 60-120 words are optimal for extraction by LLMs. Avoid long, dense paragraphs.

Rule 3: source every piece of data

LLMs favour content that cites its sources. Include the name of the institution, year and country for each statistic. Priority to European sources for the French-speaking market.

Rule 4: use structured lists

Bullet lists and numbered lists are the preferred format of LLMs for extraction. Transform your descriptive paragraphs into lists whenever possible.

Rule 5: integrate comparative tables

Well-structured HTML tables are easily parseable by LLMs. One comparative table per article significantly increases your chances of citation.

Rule 6: implement complete schema markup

JSON-LD for Article, FAQPage, HowTo and Organization. Structured data is not directly read by LLMs, but it influences ranking in RAG engines and AI Overviews.

Rule 7: add a rich FAQ

FAQ sections are an ideal format for LLMs: each question-answer pair is a self-contained, citable fragment. Include 5-8 questions per article, using the real questions your clients ask.

Rule 8: update regularly

RAG engines favour recent content. Update each flagship article every 60-90 days with new data, statistics or examples.

3. Comparison: content formats and AI citation rates

4. Before/after examples: GEO optimisation

Prof. Maarten de Rijke, chair of AI at the University of Amsterdam, confirms:

"Informational clarity is the primary selection criterion of RAG systems. Content that passes the 'self-contained paragraph' test — readable and useful without additional context — has 4 times more chances of being selected as a source."

5. AI-first publication checklist

6. FAQ — AI content optimisation