RAG AI Solutions

Retrieval-Augmented Generation (RAG) gives an AI application relevant source material before it generates a response. It can support internal knowledge assistants, document search, and customer support using an approved knowledge base. RAG can reduce unsupported answers, but it does not eliminate hallucinations. A useful RAG implementation includes document ingestion, retrieval evaluation, access controls, source attribution, and testing for incorrect or missing answers. Privacy and accuracy depend on the data, model provider, permissions, and deployment design. RAG is an application architecture, not a requirement for the marketing website to appear in AI search.