How to make your work legible to AI
Search is becoming answer engines. Being citable by machines is now a design discipline.
- Guide
- June 2026
- 7 min read
As discovery shifts from search results to AI answers, being citable by machines is a design problem. Make your work legible to AI by leading with answer-first summaries, structuring content into clear claims, adding question-and-answer pairs and structured data, and keeping a consistent account of who you are across the web.
For twenty years the goal of being findable was a rank: get your blue link near the top and people would click it. That world is ending. More and more, people ask a question and read an answer assembled by a model from sources they never visit. If a machine cannot parse and quote you, you are not on page two. You are simply absent from the answer. We built this very site around that shift.
Write answer-first
The single highest-leverage change is to put the quotable claim first, then the build-up, instead of the other way around. Human writing earns its conclusion slowly. A model extracting an answer wants the conclusion up front, in one clean sentence it can lift without surrounding context. Lead with the answer, then earn it. You lose nothing for the human reader and you become quotable for the machine.
If a machine cannot quote you in one clean sentence, it will quote someone else.
Structure for extraction
- Answer-first summaries: a one or two sentence claim at the top of every page that stands on its own.
- One claim per passage: break ideas into discrete, liftable units instead of long undifferentiated prose.
- Explicit question-and-answer pairs: phrase the real questions people ask, then answer them plainly. This is what models reach for.
- Structured data: mark up the page so machines do not have to guess, using Article, FAQPage, Organization, and the links that tie your identity together.
- A consistent entity: describe who you are the same way everywhere, so the systems building a model of you are not reconciling three different stories.
A concrete before and after
Take a typical opening line: "We have always believed that great design starts with a deep understanding of the people we serve." It is warm, and it is useless to a machine, because there is no claim to lift. Now the answer-first version: "Good design starts with intent: knowing what to make and why, before any form exists." Same idea, but the second is a clean, standalone sentence a model can quote and attribute. The first is mood. The second is citable.
This is hygiene, not a hack
None of this is gaming the system, and that is the point. Writing answer-first, structuring claims clearly, saying who you are consistently: this is just clarity for human readers, taken one step further so a machine can follow it too. The sites that win the answer-engine era will not be the ones with the cleverest tricks. They will be the ones that were clear enough to be understood by a reader who cannot infer anything you did not actually say.
Legibility to machines is becoming a design discipline, the same way responsive layout became one fifteen years ago. It feels like extra work now and it will feel like table stakes soon. Build for the reader who quotes you, not just the one who clicks.
Asked & answered
It means structuring your work so AI systems can parse, understand, and quote it: answer-first summaries, one clear claim per passage, explicit question-and-answer pairs, and structured data describing the page and the entity behind it.
Lead with a clean, quotable answer before the explanation, break content into discrete claims, add FAQ-style pairs and schema markup, and keep a consistent description of who you are across the sites these systems read.
It overlaps but goes further. Traditional SEO targets ranked links; AI legibility targets extraction and citation, which rewards answer-first writing and structured data more heavily than keyword density.