The laptop return that broke a RAG pipeline
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Case study on fixing a RAG pipeline with hybrid search, directly applicable to AI/ML systems.
The retrieval accuracy gap in RAG systems occurs when vector similarity retrieves stale policy documents or mis-scoped content, as semantic closeness doesn't ensure factual correctness. Hybrid search—a single query merging vector similarity with SQL predicates—addresses this by letting the database optimizer apply structured filters before vector scans, avoiding inefficient two-phase filtering.
Implement hybrid search queries that combine vector similarity with time, scope, or permission predicates in your RAG database schema.
You're building RAG for AI agents where silent failures from outdated or mis-scoped documents directly impact user trust and operational correctness in production.