AI docs

AI docs is the documentation category where AI-native features (semantic search, Q&A chat, auto-summary, code-example generation) are first-class — Mintlify AI, ReadMe AskAI, GitBook AI, Algolia AI Search are 2026 leaders.

Traditional docs gave you full-text search + linked TOCs. AI docs ship: semantic search (find by meaning, not keyword), Q&A chat (ask a question, get an answer with source citations), code-example generation (give me a Python example for this endpoint), summarization (TLDR of long pages), per-user history. Engineering: pages are embedded into a vector index, queries fetched + reranked, LLM generates the answer with citation. Production gotchas: citations must actually back the answer ([[citation-extraction]]), private / paywalled docs need auth-aware retrieval, multi-version docs need version filtering. AI docs are table-stakes for 2026 dev-tool launches — devs expect to chat with docs the same way they chat with ChatGPT.

When to use ai docs

• Developer-facing docs of any size.

Common mistakes

• Skipping citation — users can't trust generated answers.
• No version filtering — old version's answers leak into new version's Q&A.

FAQ

Related terms

• Search-grounded generation — Search-grounded generation is the LLM workflow where every output sentence is anchored to a retrieved source — the production pattern behind Perplexity, ChatGPT Search, Gemini AI Overview, You.com.
• Citation extraction — Citation extraction is the technique of attaching source URLs to LLM-generated claims — critical for AI search products to be trustworthy and to give users a way to verify generated content.
• AI search engine — An AI search engine answers a user's query by retrieving relevant web sources and synthesising a cited answer with a language model — the category that includes Perplexity, ChatGPT Search, Claude with web, and Gemini AI Overviews.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/ai-docs.md.