Guide
Business RAG: the AI that answers from your documents
Business RAG connects the AI to your documents without relying on the model's memory. The model does not need to “know” your procedure: it needs to find the approved version when required.
What RAG is, in plain words
RAG stands for retrieval-augmented generation. Before answering, the system searches your documents for the relevant passages. Then the language model builds the answer using only those passages.
An important distinction: RAG does not “train the AI on your PDFs”. The documents stay yours and stay the source. If you update a manual, the answer follows the new manual. That is why it is the best-suited method for AI on company documents: manuals, procedures, warranty terms.
How it works, in four steps
- Ingestion. The approved documents are chosen and their text is extracted.
- Indexing. The content is split into parts and organised by meaning, not just by keywords.
- Retrieval. For each question the system finds the most relevant passages.
- Generation. The model receives those passages and writes the answer, following precise rules: no source, no invented answer.
Quality depends on the documents and on retrieval at least as much as on the model. A good model with confusing sources gives confusing answers.
A step-by-step example
Example. An installer abroad writes in German: “the control panel shows error E14 after the update”. The manual is in Italian.
- The system searches the manual for “E14” and “update” and finds the error code table and the recovery procedure.
- The model reads only those passages and answers in German with the steps to follow.
- Below the answer, the manual page it comes from is shown.
- If the code is not in the manual, the assistant says so and collects the request for the technician.
This is the typical case of multilingual technical support, such as that of an Italian manufacturer of intrusion alarm systems that also sells abroad. More examples on the technical support page.
When you need RAG and when something else is enough
Not every search problem calls for RAG. First look at how many questions come in, how risky a mistake is and what kind of answer is needed.
| Approach | Good for | Watch out for |
|---|---|---|
| Static FAQ | A few known, stable questions | Does not cope with questions phrased differently |
| Traditional search | Well-organised archives and expert users | You must already know what to look for |
| RAG | Free-form questions across many documents | Source quality, versions, retrieval |
| Model training (fine-tuning) | Style and format of the answers | Not suited to keeping manuals and procedures up to date |
The most common mistakes
Problems almost always come before the model.
- Uploading everything without choosing. Duplicate, outdated or contradictory documents make retrieval worse.
- Not marking the valid version. If there are several revisions of the same procedure, only one should remain.
- Ignoring the “I don't know” case. The system must know what to do when it does not find enough: say so and collect the request.
- Judging by the demo. A smooth demo does not prove that the system handles difficult questions.
- Forgetting the boundaries. Internal documents and customer-facing documents can have different scopes.
- Not planning for updates. Someone has to add the new revisions, or the answers go stale.
How to set up a first test
The best test is small, difficult and measurable. Do not start from “all company documents”. Start from a controlled group of documents and real questions that today take time or an expert technician. Then measure:
- how many questions get the correct answer;
- how many find no answer in the documents;
- whether the source shown is the right one;
- how long it takes to reach the answer;
- which critical cases slip through.
Unanswered questions are the most useful data: they tell you which documents are missing. To choose and organise your sources, read the guide to the AI knowledge base.
How it works in Vucìa
Vucìa uses RAG without you having to deal with the architecture.
- You upload PDFs and web pages. Answers come from there, with the source page.
- If it cannot find the answer, it says so and takes the request.
- Phone, WhatsApp and website chat query the same knowledge base.
- 32 languages, servers in Europe.
- In the portal you see requests, minutes and unanswered questions.
With the Manuals plan, from €349 per month, our team does the set-up and, on request, we add 3D models of your products. If you only need an assistant on your website, see the business AI chatbot and the pricing: plans start from €49 per month.
Upload a manual and try RAG on your real questions. No set-up fee, cancel anytime. Want to know if your case is a good fit? Book a demo.
Activate your VucìaFrequently asked questions
Are RAG and a chatbot the same thing?
No. RAG is the way the AI retrieves knowledge from your documents. The chatbot is one of the interfaces you ask the question through. The same RAG can also serve WhatsApp and phone.
Does RAG eliminate AI errors?
No, but it reduces them, because the answer is based on passages retrieved from your documents and not on the model's memory. You still need well-kept sources, tests with real questions and a clear rule for when the answer is not there.
Can I use PDF manuals?
Yes, if the text is readable. The better organised and up to date the document, the better the retrieval of the right passages.
Do I have to train the model on my documents?
No. With RAG the documents stay the source and are consulted for every question. If you update a manual, you update the answers too.
Where is it best to start?
With a few reliable documents, a list of real questions and success criteria decided before the test. That way you quickly see whether the case deserves to be extended.