RAG stands for retrieval-augmented generation. In plain terms, the chatbot searches your documents first, then writes its answer using what it found, and can show you the source it used.
Why not just ask a general chatbot
A general language model knows a lot about the world and nothing about your fee policy, your service contract terms or your product manual. Left alone it may sound confident and still be wrong about your business.
RAG fixes this by giving the model the relevant pieces of your own material at the moment it answers.
The steps behind a RAG assistant
Your documents are split into short passages and stored in a searchable index. When someone asks a question, the system finds the passages most related to it, passes them to the language model together with the question, and asks for an answer based only on that material.
Showing the source passage next to the answer lets a person check it, which matters for policies, compliance notes and anything a customer might rely on.
What to check before you build
Start with the documents. If they are out of date, contradictory or scanned images, the assistant will inherit those problems. Decide who owns updating them and how changes reach the index.
Decide what the assistant should do when it does not know. Saying so and handing over to a person is better than guessing.
Finally, test with real questions from your staff or customers and keep a list of the ones it gets wrong. That list is how the assistant improves.
Where we can help
Our RAG and LLM service covers choosing the documents, building the index, wiring the assistant into your website, WhatsApp or internal tools, and setting up the review loop.