# QMD (Query Markup Documents)

> A format for creating searchable, structured documents that combine markdown content with query-optimized metadata for AI retrieval systems.

Category: Architecture

Source: https://chipp.ai/ai/glossary/qmd

QMD (Query Markup Documents) is a document format designed for AI-first knowledge management. It combines the readability of Markdown with structured metadata optimized for semantic search and AI retrieval systems.

QMD documents include: standard markdown content (human-readable text), frontmatter metadata (structured data about the document), query hints (suggested questions this content answers), semantic tags (topic and concept classifications), and relationship links (connections to related documents).

The format is designed to work well with RAG (Retrieval-Augmented Generation) systems by making it easier for embedding models and search engines to understand document structure, identify relevant sections, and retrieve the most appropriate content for user queries.

For AI knowledge bases, QMD offers advantages over plain text: better retrieval accuracy (metadata helps search engines find relevant content), document relationships (understanding how documents connect), query optimization (pre-defined questions improve matching), and structured sections (enabling more precise chunk retrieval).

While QMD is a niche format, the principles behind it — structured content, semantic metadata, and query optimization — are universally applicable to building effective AI knowledge bases on any platform.

## Related Terms

- [Knowledge Base](https://chipp.ai/ai/glossary/knowledge-base.md): A structured collection of information that AI systems can search and reference to provide accurate, domain-specific answers.
- [Retrieval-Augmented Generation (RAG)](https://chipp.ai/ai/glossary/retrieval-augmented-generation.md): A technique that enhances AI responses by retrieving relevant information from external knowledge sources before generating an answer.
- [Semantic Search](https://chipp.ai/ai/glossary/semantic-search.md): Search that understands the meaning and intent behind queries rather than just matching keywords, powered by embeddings and vector similarity.
- [Embeddings](https://chipp.ai/ai/glossary/embeddings.md): Dense numerical representations (vectors) of text, images, or data that capture semantic meaning, enabling similarity comparisons and search.

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This term is part of the [Chipp AI Glossary](https://chipp.ai/ai/glossary), a reference of AI concepts written for builders and businesses.

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