RAGRetrieval-Augmented Generation
An LLM pattern that retrieves relevant documents from your own data at query time and feeds them into the prompt, grounding answers in your facts.
· Reviewed by senior engineers
Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding an LLM in your own data. Instead of relying on whatever the model learned during training, you index your documents into a vector database, retrieve the most relevant chunks at query time, and inject them into the prompt as context. The model answers using your facts.
The motivation is that LLMs alone are bad at answering questions about things they weren't trained on — your knowledge base, your product catalogue, last week's policy update. Fine-tuning can teach a model new facts but is expensive, slow and hard to update. RAG is faster, cheaper and editable: change a document, re-index, and the next answer reflects it.
The failure modes are mostly retrieval failures, not generation failures. Chunks that are too big or too small, embeddings that don't capture the actual question intent, no re-ranking, no hybrid keyword/semantic search, no source citations. A bad RAG system confidently answers with the wrong document; a good one cites sources and admits when nothing matches well enough.
Devinsta builds RAG systems for internal knowledge bases, customer support assistants, product search and compliance Q&A, with evals on the retrieval layer specifically. Most RAG quality problems are solved before the LLM even runs.