Large language models are brilliant generalists and unreliable specialists. Retrieval-augmented generation (RAG) fixes that by fetching relevant passages from your own knowledge base and grounding every answer in them.
Why RAG beats fine-tuning for knowledge
Fine-tuning changes how a model behaves; it is a poor way to teach it facts that change weekly. RAG keeps knowledge in a searchable index you can update in minutes, with citations users can verify.
What a production RAG stack includes
Document pipelines with chunking tuned to your content, a vector database, hybrid keyword-plus-semantic search, re-ranking, and evaluation suites that score answer faithfulness before anything reaches users.
Common failure modes
Bad chunking, stale indexes, and missing access controls cause most RAG disappointments. Treat retrieval quality as an engineering discipline with metrics, not a demo feature. Datastrel builds and tunes RAG systems on PostgreSQL, OpenSearch, and cloud-native vector stores.