Quick summary
RAG, or retrieval-augmented generation, is an approach in which a language model first searches for relevant passages in your company documents and only then assembles an answer from them. Thanks to this, it answers based on your actual data and can cite a source for every piece of information, so you can easily verify a claim yourself.
- Answers from your documents, not from general knowledge — the model draws solely from the materials you make available to it and ignores what it learned during general training.
- Every answer cites a source for verification — you get a specific link to the passage of the document the information comes from, and you do not have to investigate where the model drew it from.
- It limits hallucinations — because the answer is assembled from retrieved text and not from the model’s memory, the risk of invented or distorted claims drops significantly.
- Data stays under your control — documents do not have to be sent to public cloud services, and if you choose local operation, they remain permanently in your infrastructure.
How RAG works
When you ask a traditional large language model a question, it answers only from what it learned during training — it does not know your internal documents, and if it does not know the answer, it may make one up. RAG reverses this principle: the model first searches your documents and only then answers.
In practice this means your documents are split into smaller semantic units and converted into so-called vector representations, which describe their meaning. These representations are stored in a vector database. When you then ask a query, the system searches the database for the passages closest in meaning to your question. The model assembles a coherent answer from these passages and automatically attaches a link to the source document. The result is an answer that rests on your data and is immediately traceable — no guesswork, no making things up.
Why RAG limits hallucinations
Model hallucinations arise mainly when the answer is generated solely from its internal memory — the model tries to guess what sounds plausible and has nothing to lean on. With RAG deployed, the model receives a specific textual context for each query that it is allowed to draw from. The answer thus arises through the controlled synthesis of existing content, not through generation from scratch. At the same time, if the answer is not present in the supplied context, the model can be set up to admit this fact rather than make something up. A person can also immediately check the original source for every piece of information — the answer ceases to be a black box.
Where RAG pays off in a company
RAG brings the greatest benefit where a company works with a large volume of unstructured text it needs fast and trustworthy access to. Typically these are situations where finding information takes minutes to tens of minutes or requires knowledge across several systems.
- An internal knowledge base — guidelines, operating manuals, technical documentation, and the company wiki accessible in plain language without lengthy trawling.
- Search across contracts and legal documentation — instantly finding a specific clause or term across hundreds of documents, including a link to the exact passage.
- Support for the customer and service team — an operator asks a query naturally and gets a synthesis from manuals, known solutions, and service history, including the source.
- Smart search across archives — mining years-old archives of e-mails, minutes, project documentation, and technical reports in which ordinary full-text search struggles.
- Materials for draft replies — the model prepares a draft reply to a tender, a technical query, or a complaint, grounded in existing materials and numerical data from the documentation.
How we deploy RAG safely
We build the deployment to match your security policy and so that you are clear about where data flows and where it stays.
- Selecting and preparing data sources — we identify the documents to be indexed into the system, set access rights so that each user sees only what they are authorised for, and decide on the frequency of index updates.
- Choosing a model and local operation — we choose the model according to the nature of the data and latency requirements. It can run in your infrastructure or in our private GPU lab in Brno, so the data does not leave a familiar environment and you do not entrust it to an anonymous cloud.
- Setting up citations and refusal rules — we define the format of source citations and the limits beyond which the model must not answer, so that it does not adopt assumptions from general knowledge.
- A pilot on your data and the move into operation — we launch a verification run on real queries, measure the accuracy of answers, and only after evaluation do we hand the system over into routine operation, where you can look after it yourself, or we continue to operate it.
Why ITHOPE
Over eighteen years in Brno we have built our own GPU lab and a team that understands not only models but the whole surrounding operational chain. With us, RAG does not mean outsourcing into a black box — we build the solution so that you understand it and can audit it.
- Our own GPU lab in Brno — the models run locally on our hardware, which we fully manage, and your data does not have to travel into public cloud environments.
- We understand both data and operation — we handle the whole context: from managing documents and backing them up, through access control, to security policies, so the result fits into your IT environment.
- We build and operate — as needed, we deliver a one-off implementation project, or take over long-term oversight and maintenance, including model updates and data re-indexing.
- A human in the loop and fair advice — we do not believe in automatic deployment without verification. If we find RAG is too complex a tool for your case and a simpler search will do, we will say so straight.
Let’s arrange a consultation
We offer a no-obligation consultation in which we go through your current documents together — their format, volume, and the typical queries you expect from the system. The point is not to deploy technology straight away, but first to find out whether RAG will really save you time, or whether a different approach will be enough.
If you are interested in a broader view of AI deployment in a company or want to understand the costs better, take a look at the article How much AI costs for a company — and then get in touch.