BUSINESS KNOWLEDGE
RAG for Business in Luxembourg: Can Your AI Find What Your Company Knows?
How retrieval-augmented generation turns business documents into useful answers, and how to test a RAG system before relying on it.
The answer is in a document somewhere. Unfortunately, “somewhere” might mean a shared drive, a mailbox, a scanned PDF or a policy that was replaced last year.
Retrieval-augmented generation, or RAG, gives an AI system relevant material to work with at question time. It can make company knowledge easier to use. Its value depends on which material it finds, who is allowed to see it and whether the answer can be checked.
A real question, a concrete chain of work
Imagine a Luxembourg distributor asks: “What is the return period for this customer?” A useful system should identify the customer’s applicable agreement, retrieve the relevant clause and give the employee an answer with a reference to the source. It should not confidently substitute a general policy if the contract says otherwise.
That is an illustrative example, not a result from a named customer. In practice, the documents, permissions and conflicting versions need to be prepared and tested for the business.
What RAG does—and what it does not
A typical setup extracts text from documents, divides it into manageable passages and builds a searchable index. For a question, it selects potentially relevant passages and provides them as context to a language model. The model generates an answer using that context and the instructions it receives.
RAG is not the same as training the model on your documents. It also does not guarantee that retrieval found the right passage or that the model interpreted it correctly. A citation is a path back to evidence, not proof that the answer is right.
| Test case | Expected behaviour | Failure to investigate |
|---|---|---|
| Two policy versions | Find the applicable version and date. | Confidently using an obsolete rule. |
| Restricted contract | Respect the user’s configured access. | Showing a passage to an unauthorised person. |
| Missing information | Say what cannot be established. | Filling the gap with an invented answer. |
Prepare the sources before chasing the model
Start with a defined collection: for example, current customer policies and a small set of approved contracts. Assign an owner, identify the authoritative version and separate documents that different teams must not share. Scanned documents may need OCR; check whether tables, footnotes and dates survived extraction.
Set an update procedure. If an agreement changes, the old passage should not keep competing with its replacement without an explicit reason. A well-organised source collection often improves a pilot more than adding a large quantity of unrelated files.
Test it like a business process
Prepare questions whose answers a subject-matter expert already knows. Include an ordinary case, a conflicting policy, a document the user cannot access, a missing answer and a poorly scanned page. Record the expected source as well as the expected answer.
- Can the user open the cited source and find the relevant passage?
- Does the answer distinguish an individual agreement from the general rule?
- Does it acknowledge missing or ambiguous information?
- Are restricted documents absent for users without permission?
- What happens after a source is changed or deleted?
From search to work with DigiRAG
DigiRAG can connect selected sources, organise information into datasets and provide source-linked answers. Access, document preparation, model choice and updates must be configured for the organisation. Supported connected sources and OCR options depend on the implementation.
The same knowledge can support an agent: for example, an authorised workflow can use a policy to prepare a response to a customer enquiry. The organisation decides which actions run directly and where review is required. Start with a narrow task, compare answers with the originals, and expand only when the process earns trust.
Questions we often hear
Does RAG stop hallucinations?
No. It gives the model material to use, but retrieval and interpretation can still fail. Test difficult cases and inspect important sources.
Can RAG use documents in several languages?
A system can be configured for multilingual material, but extraction, retrieval and answer quality should be tested with the languages and terminology your team actually uses.
