content that generative engines cite.
How to structure pages and information so they are chosen as sources by AI-generated answers.
In this article
How a generative engine "reads"
When ChatGPT or Perplexity compose an answer, they select sources they can understand and summarise without ambiguity. They reward pages where information is explicit: clear definitions, verifiable statements, orderly structure. They penalise the vague: promotional pages, texts that circle around the point, information scattered across ten different places.
The five traits of cited sources
First: a clear answer in the opening lines — the "answer first, detail after" structure. Second: explicit definitions ("X is..." works better than a thousand circumlocutions). Third: attributable numbers and facts. Fourth: structured data (schema.org: FAQ, Article, Organization) that tells the machine what it is reading. Fifth: a clear identity — who is writing, with what expertise, and from where.
The format that works
Headings phrased as questions, short paragraphs that answer immediately, a summary box, closing FAQs. It's no coincidence this is also the most readable format for a human in a hurry: optimising for AI and optimising for an impatient reader are now the same thing.
The mistake to avoid
Writing "for the algorithm" by stuffing pages with keywords. Generative models recognise — and discard — text written to manipulate them. The winning strategy is the most honest one: genuinely being the clearest, most competent source on your topic. The technique exists to make that understood, not to fake it.
- Answer first, detail after: on every page.
- Explicit definitions, attributable numbers, schema markup.
- The AI-friendly format is also the most human one.
- You can't trick a model: you become the best source.
quick answers.
How do you write content that AI cites as a source?
An answer in the first two lines, explicit definitions ("X is..."), attributable numbers and facts, structured data and an identifiable author: that is how a language model can understand and reuse the content.
What is structured data and why does it matter?
Standard labels (schema.org) that describe to machines what a page contains: they help search engines and AI understand, classify and cite content.
Does a company blog still make sense?
Yes, more than before: articles are the raw material from which generative engines learn who you are and what you can do — if they are written to answer, not to fill space.
