How to give ChatGPT your company's brain: a plain guide to RAG
Off-the-shelf AI knows the internet but nothing about your business. Retrieval-Augmented Generation (RAG) fixes that — here's how it works, in plain English.
Ask ChatGPT about your returns policy and it'll invent a confident, plausible, completely made-up answer. Not because it's broken — because it has never seen your policy. The fix has an ugly name and a simple idea: Retrieval-Augmented Generation, or RAG.
The one-sentence version
RAG means: before the AI answers, it looks things up in your documents and answers using what it found — instead of guessing from memory. Think of it as giving the model an open-book exam on your business.
Why it beats 'just train a custom model'
People assume you must retrain the AI on your data. You almost never should — it's expensive, slow, and out of date the moment your prices change. RAG keeps your knowledge in a normal, searchable store you can update any time, and the model reads the latest version on every question.
- Always current — change a document, and the next answer reflects it.
- Cited — good RAG shows its sources, so answers are checkable.
- Safer — it can be told to only answer from what it found, and otherwise say 'I don't know'.
What it can plug into
Your 'company brain' is usually scattered — and that's fine. RAG can pull from wherever it lives.
Want AI that actually knows your business?
We build grounded chatbots and copilots that answer from your real documents — no hallucinations, no nonsense. Based in Liverpool, working worldwide.
Explore our AI work →Frequently asked questions
Is RAG the same as training my own AI?
No — and that's the good news. Training bakes knowledge into the model and goes stale fast. RAG keeps your knowledge in a searchable store the model reads on every question, so it's cheaper, always current, and far easier to update.
What documents can it use?
More or less anything with text: PDFs, Word docs, your website, help-centre articles, spreadsheets, past support tickets, product data. Part of the build is connecting and tidying those sources.
Will it still make things up?
Done properly, rarely. We constrain the model to answer only from what it retrieved and to say 'I'm not sure' otherwise, and we surface sources so answers can be checked.
Is my data used to train someone else's model?
Not with the right setup. We use business-grade APIs and configurations where your content isn't used for training, and we keep credentials and data handling locked down as part of the build.