Retrieval-Augmented Generation (RAG) lets AI answer questions using YOUR data instead of its training data. It's how you build a chatbot that actually knows your business.
How it works:
1. Your documents are split into chunks and stored in a vector database 2. When a user asks a question, the system finds relevant chunks 3. Those chunks are passed to the LLM along with the question 4. The LLM generates an answer grounded in your actual documents
Why it matters for enterprise:
- No fine-tuning required: Your data stays separate from the model
- Always current: Update documents and answers update immediately
- Auditable: You can see which sources informed each answer
- Secure: Data never leaves your infrastructure (if self-hosted)
Common use cases:
- Internal knowledge base Q&A
- Customer support with product documentation
- Contract and policy analysis
- Research across large document sets
What makes RAG fail:
- Poor chunking strategy (wrong context retrieved)
- No reranking (relevant docs buried by noise)
- Missing metadata (can't filter by date, author, type)
- Ignoring evaluation (no way to measure accuracy)
Implementation timeline:
- Basic RAG prototype: 2-3 weeks
- Production-ready with evaluation: 6-8 weeks