Retrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents or data for relevant information, then uses what it finds to generate an answer. It grounds answers in your content, makes it possible to show sources, and reduces the risk of the AI inventing information.
How RAG works
- Your documents (policies, manuals, contracts, product information) are split into sections and indexed for search.
- When someone asks a question, the system finds the most relevant sections.
- Those sections are given to the AI model together with the question.
- The model writes an answer based on that content and can cite where it came from.
Business uses for RAG
- Internal knowledge assistants for staff
- Customer-support answers based on your help content
- Searching contracts, policies or technical documentation
- Onboarding new employees with answers from company guides
What makes a RAG system reliable
- Clean, current source documents with an owner who keeps them up to date
- Access controls so people only see answers from content they're allowed to read
- Answers that show their sources
- Testing with real questions before launch, and monitoring afterwards
RAG vs training a custom model
For most business knowledge, RAG is faster, cheaper and easier to keep current than training a custom model, because updating the answers only requires updating the documents.
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