What Is Retrieval-Augmented Generation (RAG)?
Retrieval-augmented generation (RAG) is a technique that retrieves relevant information from a knowledge base and supplies it to a large language model as context, so answers are grounded in trusted, current data.
Retrieval-augmented generation (RAG) is a technique that retrieves relevant passages from a knowledge base and supplies them to a large language model (LLM) as context, so the model answers from trusted, current information instead of only its training data.
Why it matters
A general LLM only “knows” its training data up to a cutoff date and nothing private. RAG lets a model answer from your documents, policies, and products — and cite sources — which dramatically reduces hallucinations and makes answers traceable. It’s the most common pattern for reliable enterprise generative AI.
How it works (in brief)
Documents are ingested, split into chunks, and converted into vector embeddings stored in a vector database. At query time, the system retrieves the most relevant chunks and passes them to the LLM to generate a grounded answer.
Related
See RAG development, the RAG Implementation Guide, and when to choose RAG vs. fine-tuning.
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