RAG that actually works: beyond the vector database

Retrieval-augmented generation is deceptively simple to prototype and surprisingly hard to get right. The failure mode is almost always retrieval quality, not the language model.

Chunking is a design decision

Naive fixed-size chunking destroys the semantic coherence your retriever depends on. We chunk along document structure and preserve context with overlaps and metadata.

Hybrid search beats pure vectors

Dense vectors capture semantics but miss exact matches. Combining them with keyword search and a reranking pass consistently outperforms either alone.

Measure retrieval, not just answers

Before you blame the model, measure whether the right context was even retrieved. Recall@k on a labeled set is the single most useful metric we track.