Retrieval-Augmented Generation (RAG) is a critical AI technique that anchors a Large Language Model (LLM) to verifiable, external data, virtually eliminating guesswork and hallucinations. Instead of relying solely on its training data, the agent first retrieves relevant, up-to-date information from a secure knowledge base using Embeddings and Semantic Search, then uses that context to generate a precise, grounded answer.
Spiral Scout implements RAG to ensure all AI-driven tools provide trustworthy results based on the client’s most current and accurate data.
