What Is Retrieval-Augmented Generation

The Problem RAG Solves

Large language models are trained once and answer from memory. They can hallucinate, go stale, and know nothing about your private documents. Retrieval-Augmented Generation (RAG) fixes that by looking up relevant text before the model writes its answer.

How RAG Works

RAG adds a retrieval step to normal generation. The user question first searches your knowledge base, the best matches are handed to the model as context, and the model answers from that context instead of from memory.

Why Teams Choose RAG

• Fresh answers - Update the knowledge base, not the model.
• Private data - Your documents never train a model.
• Fewer hallucinations - The model is told to rely on the notes.
• Cheaper than fine-tuning - No training runs are needed.

Where RAG Fits

RAG powers support bots, document Q&A, internal search, code assistants and any app where answers must reflect specific content the model never saw.

Key Points

• RAG retrieves evidence before generating.
• Answers stay grounded in your own documents.
• Knowledge can be updated without retraining.
• It is the standard architecture for LLM knowledge apps.