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Docs AI glossary

Short, direct definitions of the terms behind AI-powered documentation: retrieval, answer engines, deflection, and how docs get read by machines.

  • Ticket deflection

    Ticket deflection is the practice of resolving user questions through self-service resources, such as documentation or an AI assistant, before they become support tickets.

  • RAG (retrieval-augmented generation)

    Retrieval-augmented generation (RAG) is a technique where an AI model retrieves relevant passages from a knowledge base, such as product documentation, and uses them to generate a grounded answer.

  • Answer engine

    An answer engine is a system that responds to a question with a direct, synthesized answer instead of a list of links; ChatGPT, Perplexity, and Google AI Overviews are examples.

  • GEO (Generative Engine Optimization)

    Generative Engine Optimization (GEO) is the practice of structuring content so AI answer engines like ChatGPT, Perplexity, and Google AI Overviews cite it in their responses.

  • llms.txt

    llms.txt is a proposed standard file, served at a site's root, that gives large language models a curated markdown index of the site's most important pages.

  • MCP (Model Context Protocol)

    The Model Context Protocol (MCP) is an open standard that lets AI assistants such as Claude connect to external tools and data sources, including documentation, through a common server interface.

  • Hallucination

    A hallucination is a confident but false statement produced by an AI model, such as a chatbot inventing a configuration option that does not exist in the documentation.

  • Grounding

    Grounding is the practice of constraining an AI model's answers to a trusted source, such as your documentation, so every claim can be traced back to real content.

  • Docs-as-code

    Docs-as-code is an approach that treats documentation like software: written in plain text, stored in version control, reviewed in pull requests, and published through CI pipelines.

  • Deflection rate

    Deflection rate is the percentage of user questions resolved by self-service, such as a docs AI chatbot, that would otherwise have become support tickets.

  • AI crawler

    An AI crawler is a bot, such as GPTBot, ClaudeBot, or PerplexityBot, that fetches web content to train language models or to retrieve sources for AI-generated answers.

  • Embedding

    An embedding is a numeric vector that captures the meaning of a piece of text, letting a docs search system match questions to relevant passages by semantic similarity rather than keywords.

  • Chunking

    Chunking is the process of splitting documentation into small, self-contained passages before indexing, so a retrieval system can find and return the exact section that answers a question.

  • Answer-first writing

    Answer-first writing is a documentation style that states the direct answer in the first sentence of a page or section, before any background or context.

  • Content gap

    A content gap is a question users ask that your documentation does not answer, often discovered through unanswered chatbot queries or failed searches.

  • Federated search

    Federated search is a single search experience that queries content living in multiple separate systems and returns one combined set of results.

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