RAG

RAG (retrieval-augmented generation) is an approach where a language model doesn't answer from memory alone. First, the system finds passages in a knowledge base that match the question. Then it passes them to the model along with the prompt, and the model builds its answer from them.

What RAG gives you:

• Answers from internal docs the model never saw in training
• Up-to-date data without retraining the model
• Source links you can easily check
• Fewer made-up facts

This is how AI agents in Pachca answer from a knowledge base: the agent finds the answer in company docs and conversations and cites the source. New hires don't have to wait for a teammate to free up, and your chat and thread history becomes shared company context.