Short answer
RAG (retrieval-augmented generation) is an approach in which AI first finds the relevant information in your documents or database and then answers based on it. That way AI can answer questions about your products, rules and customers, and the answer can point to its source.
Why RAG is needed
A language model on its own doesn’t know your prices, procedures or contracts. You could try to “teach” everything to the model, but that’s expensive and quickly goes out of date. RAG solves it more simply: every time a question is asked, the system first finds the relevant documents, and only then does the model write the answer.
How it works
- Company documents are prepared for search (split into passages and indexed).
- The user asks a question.
- The system finds the most relevant passages.
- The model writes an answer based directly on them and can cite the source.
Examples
- An employee assistant that knows internal procedures and instructions.
- Customer support that answers from product documentation.
- Searching legal or technical documents by asking questions in plain language.
Benefits
Answers stay current (you update the documents, not the model), there are fewer hallucinations, and you can control which documents each user can access. More: how to build an AI assistant for company documents.