12 AEO practices to make your documentation AI-ready

Expo’s documentation team describes Answer Engine Optimization (AEO) as adapting technical documentation for AI coding assistants and answer engines that retrieve live content to answer questions or perform tasks, rather than relying on traditional search rankings or a model’s fixed training data. Its approach includes publishing a concise llms.txt index, offering Markdown versions of pages through content negotiation, URL suffixes, and HTML discovery hints, converting rendered MDX content into reliable Markdown, and adding factual corrections to address outdated model knowledge. The team also uses schema.org JSON-LD generated directly from page content, declares policies for search, AI training, and retrieval use, invites agents to report documentation gaps, and enforces consistent writing with linting. Additional measures include auditing pages with Lighthouse’s Agentic Browsing tools and AFDocs scores, preventing React hydration mismatches that could expose inconsistent page structures, and maintaining a clean accessibility tree for efficient agent interpretation. Although standards and infrastructure may evolve or automate many of these practices, the post argues that accurate, structured, accessible, and consistently maintained documentation remains essential for both human developers and AI agents.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.