Featured Snippet

Google generates featured snippets by identifying a query with a clear answer, then extracting the best-matching passage from a page ranking on the first page of results. Paragraph snippets answer definitional and how questions; list snippets capture steps and rankings; table snippets surface structured comparisons. The source page does not need to rank first — it needs the cleanest extractable answer.

Winning formats are consistent: a heading that mirrors the query, an immediate 40-60 word answer, and markup that matches the content type — ordered lists for steps, real tables for comparisons. This is passage extraction in its earliest mainstream form.

Featured snippets were the first major case of search lifting an answer out of a page, and they normalized zero-click search — users get the answer without visiting. AI answers generalize the pattern: instead of one extracted passage, engines synthesize several, with citations. Content already optimized for snippets tends to perform well in AI retrieval because both reward the same property: passages that answer a question completely on their own.

There is direct overlap too. Google AI Overviews frequently draw on the same kinds of concise, well-structured passages that win snippets, and pages holding snippets for a query are strong candidates for AI Overview citations on related queries.

Target question queries with a dedicated section per question: question as the h2 or h3, direct answer first, detail after. Match format to intent — paragraphs for definitions, numbered lists for processes, tables for comparisons. Add FAQ schema where genuinely applicable. The same sections then compete in AI retrieval, so one structural investment serves both surfaces; tracking which engines actually cite those sections shows where the investment is paying off.