RAG explained
Retrieval-augmented generation, or RAG, is a pattern that retrieves relevant information from an external knowledge source and supplies it to a language model when generating an answer. It helps applications use private or current information without retraining the entire model.
How a RAG pipeline works
Documents are collected, cleaned and divided into useful chunks. An embedding model converts chunks into numerical vectors stored in a vector database. When a user asks a question, the system retrieves similar chunks, builds a prompt with that context, and asks the LLM to answer from the supplied evidence.
RAG versus fine-tuning
RAG is useful when knowledge changes or answers should cite documents. Fine-tuning changes model behaviour and style through training examples; it is not usually the best way to inject frequently changing facts. Many robust applications combine good prompting, retrieval and selective fine-tuning.
Common RAG problems
Weak document parsing, chunks that are too broad or narrow, poor metadata and ambiguous queries can damage retrieval. The model can also ignore evidence. Strong systems use hybrid search, reranking, filters, citations, access control and evaluations for retrieval recall and answer faithfulness.
A practical RAG project
Build a knowledge assistant over a small, well-defined document collection. Store source metadata, return citations and create a test set of real questions. Measure whether the correct evidence is retrieved and whether the answer stays supported by that evidence.
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