Most business owners have tried a chatbot that confidently answers with something wrong. RAG, short for retrieval-augmented generation, is the approach that fixes much of that. In plain English, it makes the chatbot look things up in your documents before it answers.
How RAG works
- Your documents (policies, manuals, price lists, FAQs) are split into small passages and indexed.
- When someone asks a question, the system finds the most relevant passages.
- Those passages are given to the language model, which writes an answer based on them.
- The answer can cite its sources, and the assistant can say “I do not know” when nothing relevant is found.
Why it beats a generic chatbot
- Accuracy: answers come from your material, not the open internet.
- Control: update a document and the answers update.
- Trust: citations let staff and customers verify.
Where it helps most
- Customer support questions that repeat daily.
- Internal knowledge for staff: HR policy, procedures, product specs.
- Enquiry triage: classify, summarise and route leads.
- Quoting support, where answers depend on a price list or rules.
What to put in place
Set a hard monthly spend cap, log every conversation, add a clear handoff to a human, and limit abuse with rate limits and bot checks. For UK and EU businesses, be clear about what data is processed and where.
What it costs to run
Running costs depend on usage, but with sensible limits and the smallest model that meets your accuracy bar, they are often modest compared with the staff time saved.
You can try working demos on this site, or read how I build assistants and book a free call to pick a first use case.
