Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation combines the power of large language models with your organization's specific data. When a user asks a question, RAG first searches a knowledge base (documents, databases, wikis) for relevant information, then feeds that context to the LLM to generate an accurate, grounded answer. This approach reduces hallucinations, keeps responses current without retraining, and lets businesses build AI assistants that answer questions using their actual policies, products, and procedures.