What is RAG development and how does it work?
RAG development is the engineering of retrieval-augmented generation systems, which answer questions by first retrieving relevant passages from your own documents and data, then having a language model write an answer grounded in those passages. The model does not need to have learned your content; it reads the retrieved text at answer time. A production build has several parts. Ingestion connects sources such as documents, wikis, tickets, catalogues and databases, cleans them, and chunks them along their own structure, attaching titles and headings. Retrieval combines vector search for meaning with a keyword index for exact terms like product codes, then re-ranks results and applies permission filters. Generation returns answers with citations and declines when the sources are silent. Evaluation scores retrieval and answer quality on a set of real questions after every change. Operations re-index sources as they change and monitor cost, latency and quality. RAG suits knowledge that changes and answers users must verify; fine-tuning suits a fixed style or a narrow, stable task.
