RAG (retrieval-augmented generation) is how you make an AI answer from your documents instead of its general training. The bot first finds the relevant passage in your files, then writes an answer based on it — so it stays accurate and stops inventing facts.
RAG in plain English
Imagine an assistant that, before answering, opens your manual to the right page and reads it. That's RAG: retrieve the relevant text from your knowledge base, then generate an answer grounded in it. The model supplies fluent language; your documents supply the facts.
How it works under the hood
- Your documents are split into chunks and indexed by meaning.
- A user asks a question.
- The system finds the most relevant chunks.
- The model answers using only those chunks, and can cite them.
Why it doesn't make things up
A plain AI model guesses from general training and can confidently state wrong things. RAG anchors every answer to your real content, so it reflects your current prices, policies and procedures.
Where it shines
- Customer support over your help docs.
- Internal assistant over policies and runbooks.
- Sales bot that quotes from your live price list.
Often delivered inside an AI Telegram bot.