Ask ChatGPT about your return policy and it will confidently make something up. That’s the problem retrieval-augmented generation (RAG) solves: it lets AI answer using your documents and data, with sources.
RAG in one sentence
Before the AI answers, it searches your knowledge base, pulls the most relevant passages, and uses them to write an answer — like an open-book exam instead of a memory test.
How it works
- Ingest — your documents (PDFs, Google Drive, Notion, help-desk articles, website) are split into chunks.
- Embed — each chunk is converted into a vector, a numeric representation of its meaning.
- Store — vectors go into a vector database such as Pinecone or Supabase pgvector.
- Retrieve — when a question comes in, the system finds the most relevant chunks by meaning, not just keywords.
- Generate — the LLM writes an answer using those chunks and cites them.
Why not just fine-tune a model?
Fine-tuning teaches a model style and patterns, but it’s a poor way to store facts that change. RAG is cheaper, updates instantly when documents change, supports permissions, and shows sources. For most business knowledge, RAG is the right tool.
Business use-cases
- Customer support bots that answer from your real docs.
- Internal assistants for SOPs, HR policies and onboarding.
- Sales enablement — instant answers from product specs and case studies.
- Legal & compliance — search contracts and regulations in plain English.
What makes RAG good (or bad)
Most failed RAG projects fail on retrieval, not the AI model. The difference is in chunking strategy, hybrid (vector + keyword) search, re-ranking, metadata filters and — critically — an evaluation set of real questions to measure accuracy before launch.
What does it cost?
A production RAG system typically starts in the low thousands of dollars to build, plus modest monthly hosting and usage. See our RAG systems service or get a scoped quote.