RAG Metadata Filtering—Targeted Retrieval for Structured Content

I will build a RAG system with metadata filtering — attaching structured metadata (date, category, author, department, product, or any attribute) to your document chunks at ingestion, and implementing pre-retrieval metadata filters that constrain retrieval to the subset of your knowledge base relevant to each query before semantic similarity search runs. Metadata filtering transforms RAG from a flat knowledge search into a structured knowledge retrieval system — a user asking about "Q3 2024 marketing policies" should retrieve only marketing department documents from Q3 2024, not semantically similar content from other departments or time periods; metadata filters enforce this precision that semantic similarity alone cannot.

The build covers metadata extraction and attachment at ingestion, filter schema design for your attribute set, dynamic filter generation from query intent, pre-filter retrieval configuration, and retrieval precision evaluation with and without filtering.