Off-the-shelf AI chatbots often hallucinate facts when asked specific questions about proprietary company documents, legal contracts, or technical manuals.
Retrieval-Augmented Generation (RAG) solves this fundamental limitation by inserting a domain-specific vector search step between user queries and AI model generation.
When a user asks a question, the RAG engine first queries a vector database (such as Pinecone or PostgreSQL pgvector) to retrieve exact, relevant document chunks. These verified passages are provided to the LLM as explicit context.
By grounding AI responses in real, verifiable company data, businesses automate complex contract analysis, customer support lookups, and technical documentation search without data leakage or hallucinations.