Retrieval-Augmented Generation

Ground LLMs in your own data without fine-tuning.

10 min read

RAG retrieves relevant documents at query time and stuffs them into the model's context for grounded answers.

A typical pipeline: embed → vector search → rerank → prompt → generate.

RAG reduces hallucination and lets you update knowledge without retraining.