What Is a RAG System and How Do You Build One?

RAG lets an LLM answer by 'looking at' documents you supply, beyond its training data.

Goals: reduce hallucinations and surface answers grounded in your company's current, specific knowledge.

1) Collect documents (PDF, Confluence, Notion, Google Docs). 2) Chunk. 3) Embed to vectors. 4) Store in Pinecone / Qdrant / pgvector.

5) On a question, retrieve relevant chunks. 6) Pass them to the model as context. 7) Return cited answers to the user.

Wrong chunking strategy kills answer quality. Document hierarchy is the most decisive first-iteration call.

Security and permissions: user A must not access user B's docs. Skipping this layer triggers compliance risk fast.

Legal contract comparison, HR policy Q&A, technical-support knowledge base, financial report analysis, sales enablement chatbots.

RAG is valuable when the problem has rich, retrievable context — not 'AI everywhere.'