What is RAG (retrieval-augmented generation)?

Without RAG, a language model answers questions from its training data, which is a statistical summary of the internet. It does not know your company's policies, your product specifications, or your compliance rules. When it encounters a question about your specific business, it guesses. Confidently. Often incorrectly.

RAG fixes this by adding a retrieval step before generation. The user asks a question. The system searches your verified knowledge base (policies, manuals, databases, approved documents). The relevant information is retrieved and handed to the model along with the question. The model answers based on what it was given, not what it remembers.

The result is dramatically more accurate answers with citations you can verify. Instead of 'our refund policy allows 30 days' (which may or may not be true), you get 'according to Policy Document 4.2.1, the refund window is 30 business days for standard purchases.' The source is traceable. The answer is verifiable.

PRISM uses RAG as one of four layers in its reliability architecture. Retrieval grounding provides the factual foundation. Structured outputs constrain the format. Business-rule validation checks the answer. Deterministic logic handles the final decision. Together, they turn a capable but unreliable model into a system your operations can trust.