Preparing for AI search is not about repeating keywords or manipulating an answer engine. It is an editorial task: connect a reader’s question, the document’s answer, and the evidence that supports it.
Define the question each document answers
Use a title that sets a clear scope and an opening paragraph that gives the primary answer. Separate conditions, evidence, exceptions, and next actions into visible sections. Mixing unrelated topics makes provenance difficult for both people and systems.
Give tables and figures their context
Record the unit, period, population, source, and review date beside each figure. Explain the meaning of rows and columns in text. Add a short statement of the main chart relationship and its limits. Do not remove inconvenient conditions to make a number more prominent.
Publish ownership and update information
Name the responsible team, last review date, and original source. When the material changes, explain what changed at the same stable location. Link retired documents to the current version rather than leaving old figures without context.
Feat AI publicly presents a capability to structure PDFs, data, and web pages for AI search and citation. A document structure does not guarantee citation or ranking in a particular service.
Validate with questions before publishing
- Does the opening paragraph answer the main question?
- Are units, periods, and sources visible around each figure?
- Did editing introduce a claim absent from the source?
- Can a reader find the owner and review date?
- Does each important claim have inspectable evidence?
- Is there a stable URL to return to when an answer is wrong?
A verifiable structure benefits human readers first. It also gives automated systems a clearer route back to the source.