What is RAG and how does it work: a guide for companies that want AI over their documents

ChatGPT knows a lot about the world and nothing about your company. It does not know your procedures, contracts, price list, technical documentation or last year's decisions. And when it does not know, it often makes things up. RAG is the technique that solves both problems.

RAG in one sentence

RAG (Retrieval Augmented Generation) means the AI searches your documents before answering, takes the most relevant parts and writes the answer based on them, citing the source.

Imagine a colleague who has read all your company documentation, has perfect memory and always says "this is in the Employee Handbook, section 12". That is a good RAG assistant.

How it works, step by step

1. Collecting documents: PDF, Word, Excel, scans, intranet, Confluence, SharePoint, email.
2. Preparation: cleaning text, OCR for scans, removing duplicates, adding metadata (date, author, type, version).
3. Chunking: documents are split into meaningful units, for example by legal article or manual chapter.
4. Vectorization: each chunk is turned into a list of numbers that describes its meaning (an embedding) and stored in a vector database.
5. Retrieval: a user's question is also turned into a vector, and the system finds chunks with the closest meaning.
6. Generation: the AI model receives the question and the retrieved chunks and writes the answer based only on them.
7. Citations: sources are shown with the answer so the user can verify.

What "data vectorization" means and why it matters

Classic search looks for the same words. If a document says "notice period" and you ask "how long do I have to keep working after I resign", keyword search will probably miss it. Vector search will not, because it compares meaning, not words. That is why RAG feels "smart".

Why RAG does not make things up (as far as possible)

The model gets a clear instruction: answer only from the provided passages, and if the answer is not there, say you do not know. In addition:

• every answer has a source,
• the system measures how relevant the retrieved content is,
• unanswered questions are logged so the documentation can be improved.

Where RAG delivers the most value

• Law and tax: searching regulations, official opinions and case law. We built such a system for a large consultancy.
• Internal knowledge base: procedures, policies, HR questions, onboarding.
• Technical documentation: manufacturing, service, IT support.
• Sales and customer support: product specs, price lists, terms.
• Contracts: finding clauses, comparing versions, deadlines.

RAG or fine-tuning?

Fine-tuning changes model behavior (style, format), but it is not a good way to make a model "learn" facts that change. RAG is better for company knowledge because documents are updated instantly, without retraining. More in RAG or fine-tuning.

Security and privacy

A good RAG system respects permissions: users only get answers from documents they can access. Data can stay in an EU cloud, on your server or fully on-premise with local models.

Cost and timeline

We build a prototype on a sample of your documents in 2 to 4 weeks, an MVP in about 2 months and larger systems in 3 to 5 months. Estimate vectorization cost with our RAG calculator.

Next step

See our AI document assistant service or send us an inquiry with a short description of your documentation. We will build a prototype on a sample so you see results before deciding.