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RAG and search

Metadata filtering

A retrieval step that includes or excludes documents using structured attributes such as department, date, source, document type, region, or access group.

Business example

A finance employee asks for the current travel policy. The system filters to published policy documents available to that employee and effective on today's date, then ranks the matching passages instead of mixing drafts, obsolete versions, and unrelated departments.

How it works

  1. 1.Ingestion stores each document or chunk with structured metadata alongside its searchable text and vector.
  2. 2.A query supplies exact conditions, for example department equals Finance and effective date is after January 2026.
  3. 3.The search engine applies those conditions before or after keyword or vector retrieval, depending on the chosen filter mode.
  4. 4.Only eligible passages are ranked and passed to the answer model.
  5. 5.Teams test both relevance and access behavior because a useful filter must not hide valid evidence or admit restricted content.

Common misconceptions

Metadata filtering is the same as semantic search.
Semantic search ranks material by meaning. A metadata filter first defines which records are eligible using structured conditions; the two mechanisms can work together.
Adding a department tag is enough to enforce every permission.
Filters only enforce the attributes and identity rules the system actually maintains. Authorization still needs a complete, tested access model at retrieval time.

Related concepts

Retrieval-Augmented Generation (RAG)Vector databaseSemantic searchHybrid searchChunking

Related reading

Enterprise AI permissions: how to stop employees seeing information they should not
RAG is not uploading PDFs: permissions, freshness, traceability, and reliable sources
How to evaluate internal AI search tools: a 15-question checklist

Sources

Reviewed:

Reviewed by: Javier Chulvi Bernad · LLM Engineer · Madrid