Glossary
Retrieval-Augmented Generation (RAG)
An AI technique that grounds a language model’s answers in your own documents by retrieving relevant context at query time.
Retrieval-Augmented Generation (RAG) is a technique for making large language models answer from your data instead of only their training. At query time, the system retrieves relevant chunks from your documents and feeds them to the model as context, so answers are grounded and current.
RAG is the backbone of most production LLM features — internal assistants, support bots, and search — because it reduces hallucination and lets you update knowledge by updating documents, not retraining a model.
Building a reliable RAG system is a core skill of AI engineers, spanning embeddings, vector search, chunking strategy, and evaluation.