TL;DRAI

Pre-filtering applies hard predicates on metadata, reducing vector search from millions to hundreds of chunks and preventing multi-tenant boilerplate from polluting RAG results. It is the most economical recall win without embedding upgrades: a simple pre-search filter that transforms accuracy but few teams measure it.

Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production

Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go

My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools

Me: xgabriel.com | GitHub

A support agent asks the bot a question about a customer's enterprise contract. The bot retrieves the top 10 chunks by cosine similarity across the whole corpus. Two of them are from a different customer's contract that happens to use near-identical boilerplate language. The model writes a confident answer citing a clause that does not exist in the customer's actual agreement.

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Metadata Filtering Before Vector Search: The Recall Win Nobody Measures

Pre-filter by metadata to shrink the search space before the vector index runs. The recall lift, the cardinality trap, and the code.

sabato 13 giugno 2026 New tab

TL;DRAI

1,626 words~7 min read

Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production

Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go

My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools

Me: xgabriel.com | GitHub

Metadata Filtering Before Vector Search: The Recall Win Nobody Measures

Metadata Filtering Before Vector Search: The Recall Win Nobody Measures

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