Start RAG Projects Locally to Fix Retrieval Before Adding Infrastructure

RAG (Retrieval-Augmented Generation) projects often accumulate complex infrastructure—vector stores, queues, object storage—before retrieval quality is properly validated, making failures hard to diagnose. A developer-focused approach recommends starting small: use real documents people already rely on, ask questions with known answers, and inspect returned passages directly. Effective retrieval testing should include queries with exact terms, context-dependent questions, and at least one unanswerable query to verify the system handles gaps transparently. Hybrid retrieval—combining semantic vector search with lexical keyword matching—is recommended because each method covers the other's weaknesses, especially for product codes, names, or domain-specific abbreviations. A lightweight local setup using SQLite with FTS5 and a vector extension can be sufficient to validate document processing and chunking logic before scaling to production infrastructure like PostgreSQL and pgvector.