Does a company need its own IT department for RAG?

No. The provider of the solution takes care of the technical set-up; within the company, the work comes down to supplying the documents and testing with real questions from everyday work. For users, RAG feels like an ordinary chat.

Why does a RAG answer always name a source?

Because the system retrieves specific passages from specific documents before writing the answer, it always knows where a piece of information comes from. These sources are shown alongside the answer, so every statement can be checked against the original document.

What happens with RAG if the answer isn’t in any document?

A properly set-up RAG system will say that the stored documents contain no information on the subject, rather than making something up. This honest admission of a gap in its knowledge is one of the biggest advantages over chatbots that answer freely.

How up to date are the answers from a RAG system?

As up to date as the stored documents. New or changed documents are continuously added to the search index and can be used in answers from then on – there is no need to retrain the AI.

What is RAG? Retrieval-augmented generation explained simply

Q: What does RAG stand for?

RAG stands for retrieval-augmented generation – text generation backed up by a search. It describes a process in which an AI, before answering, first looks up relevant passages in a stored set of documents and bases its answer on those passages alone.

RAG (retrieval-augmented generation) is a process in which an AI, before answering, first looks up the relevant passages in a stored set of documents and bases its answer solely on them. Put in one sentence for management: the AI doesn’t answer questions from memory – it looks things up in your own documents first. That makes the answers verifiable and up to date, and far less likely to be made up.

In brief

RAG combines a search of your own documents with the language skills of an AI model.
The process has three steps: search, enrich, answer.
Because the answer is based on specific passages, every statement can be backed up with a source.
RAG is not training: new documents can be queried straight away without changing the model.
It reaches its limits where documents are missing or contradictory, or where knowledge was never written down.

How does the librarian analogy explain RAG?

Imagine an experienced librarian who looks after a huge company library. When you ask them something, they first go to the shelves, pull out the three relevant files and read the key passages – and only then answer your question, with the pages open on the desk in front of them.

An ordinary chatbot without RAG works differently: it is like a well-read conversation partner who read a great deal years ago but has no access to your library right now. It answers from memory – often eloquently, sometimes correctly, occasionally making things up – and it knows nothing at all about your company’s own files.

RAG turns the conversation partner into the librarian. The language model (LLM, large language model – an AI program that has learned from vast amounts of text to understand and produce language) still supplies the skill with words, but the facts come from the shelves of your own library.

What are the three steps in RAG?

Every RAG answer is produced by the same three-stage process, which runs in a fraction of a second and is invisible to users. You don’t need any maths to understand it.

Search (retrieval): The system searches the stored documents for passages that match the question. It searches by meaning, not just by exact wording – a question about the “notice period” also finds the clause that talks about “terminating the contract”.
Enrich (augmentation): The passages it finds are passed to the language model together with the original question – like the open files the librarian spreads out in front of them.
Answer (generation): The language model writes a clear answer based on those passages and shows them as sources.

The order is what matters: search first, then answer. The model is instructed to stick to the passages it has been given – and if there is nothing relevant in them, it is meant to say so rather than improvise.

All users see of this is a chat window. They type in a question, get an answer with a reference to the source and open the original document if they need to – the three steps in the background require neither training nor technical knowledge.

Why does RAG reduce hallucinations?

“Hallucinations” is the term for language models producing statements that sound convincing but are wrong – invented facts, figures or sources. A model answering freely has no way of checking whether its memory is correct; it produces the most likely-sounding continuation, not the documented truth.

RAG tackles this problem at the root, because the answer is tied to specific sources. Instead of reporting from memory, the model is supposed to summarise the passages in front of it – a task language models handle far more reliably than free recall.

That doesn’t make hallucinations disappear altogether, and honesty demands saying so clearly. A RAG system can also summarise sources badly or lose nuances. The practical difference lies in being able to check: because every answer refers to a source document, checking takes one click instead of your own research.

How does RAG differ from training an AI?

“We train the AI on your data” sounds similar but is a fundamentally different approach – and one that is unsuitable for most companies. Training or fine-tuning changes the internal parameters of the model itself; the knowledge is, in effect, built permanently into the model.

Criterion	RAG	Training / fine-tuning
New documents	Can be queried immediately	Only after retraining
Source	Source for every answer	Not possible – knowledge is “baked in”
Removing data	Deleting the document is enough	Hardly possible without retraining
Effort	Set-up, then low on an ongoing basis	High and recurring
Control	Basis of the answer visible at any time	Behaviour hard to trace

For answering questions from your own company documents, RAG beats training on almost every count. Deletion carries particular weight: removing a document from a RAG knowledge base takes one action – getting trained-in knowledge back out of a model is practically impossible.

Training has its place elsewhere, for example when a model needs to adopt a particular writing style or specialist terminology. Factual knowledge that keeps changing, on the other hand, belongs in the documents, not in the model’s parameters.

Where does RAG reach its limits?

RAG can only find what is in the documents. Practical experience that was never written down – the technician’s knack, the history of a customer relationship – stays invisible to any system until it is captured, at least as a note or voice recording.

Contradictory and outdated documents remain a weak spot. If there are three versions of a price list, the system can find and name all three but can’t reliably decide which one applies. Basic housekeeping in your file storage – marking or weeding out obsolete versions – is still the company’s job.

How a question is asked also affects the quality. Very vague questions (“What was the story with that project?”) produce weaker results than specific ones (“Which payment terms were agreed in the Musterbau project?”). In our experience, users get the hang of it quickly, but RAG is no magic wand for unclear requests either.

Finally, the search itself has its pitfalls. Questions whose answer only emerges from many documents – such as an analysis of all projects in a given year – stretch the approach more than a targeted, specific question. RAG excels at looking things up, not at statistical analysis of entire document collections.

What does RAG mean for companies in practice?

For small and medium-sized companies, RAG is the technical foundation that makes AI document search fit for business use in the first place: all your documents rather than individual uploads, continuously up to date rather than frozen copies, and a source rather than blind trust. The guide Using ChatGPT with your own data looks at the alternatives and why they fall down in long-term use.

When comparing providers, it is worth checking the quality features typical of RAG: does the system genuinely answer only from your own documents? Does every answer lead to a source you can check? Are scans captured with text recognition (OCR, the automatic conversion of images into searchable text)? And is the data stored on EU servers, with processing governed by contract?

The most reliable way to find out whether a solution meets these criteria in your company is to test it with your own documents and real everyday questions. junova is built on this RAG principle, and working through the answers to real questions with your own documents is a fixed part of the introduction – so the benefit is checked before the team relies on it.