The acronym RAG comes up in every conversation about AI at the company. Below: What does that mean in one sentence, what problem it solves, how it works „under the hood” (no math), how much it costs, and when No It's worth getting into.
Retrieval-Augmented Generation (RAG) combines retrieval of relevant information with text generation to produce more accurate and contextually rich outputs.
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RAG (Retrieval-Augmented Generation) to Artificial intelligence that first searches your documents before answering., and only then constructs a response based on the found excerpts – with the option to indicate sources.
The rest of the text is an elaboration of this definition – so that you can consciously talk to the provider.
What problem does RAG solve?
Imagine hundreds of files: agreements, addendums, offers, procedures. Question: „What are our contractual penalties in our relationships with client ABC?”
| Way | What is happening |
|---|---|
| Without AI | Someone browsed the archive – hours. |
| Regular ChatGPT | Model Doesn't know Your files; at most general info from the internet. |
| Chat + pasting one contract | It works, but you have to know which file; attachment in another file – easy to miss. |
| RAG | System Sam searches the repository, collects excerpts from contracts and annexes, responds with annotations for documents and pages. |
RAG is the difference between „a smart chat without your archive” and „an assistant with an index of your entire database.”.
Connecting to the bigger picture AI Architectures from Chat to RAG We describe separately – here we focus on Mechanic RAG.
How it works – three steps (simply)
1) Document preparation (rollout, in a loop)
- File (PDF, DOCX, scan) → text when scanning OCR).
- Division into Smaller pieces It's easier to get to the heart of the question.
- Replace excerpts with numerical representations (embeddings) – similar meaning = „closer” in veCTOr space.
- Record in veCTOr base optimized for fast similarity.
That's why garbage in (bad scans, messy names) yield poor results - it's not magic.
2) User question
The question is also converted into a veCTOr → search most similar excerpts from the base. One looks for sense, not only identical words (e.g., „contractual penalty” and „penalization” can overlap).
3) Generating responses
Selected excerpts + question go to a large model (e.g., GPT-4, Claude). The model should not make up instead of quoting – but always It is worth checking the footnotes, especially in law and finance.
What RAG provides in practice – an example
Question: „What SLAs did we promise customer ABC in the recent agreements?”
RAG response schema:
In the 2024 framework agreement, response time is 4 hours, resolution is 24 hours [1]. In the June 2024 addendum for critical services, the response time is 2 hours [2].
[1]Agreement_ABC_2024.pdf, page 8
[2]Annex_06_2024.pdf, page 2
Without RAG - either human memory or manual search.
RAG vs. Plain Chat - Table
| Feature | ChatGPT without a database | Chat + uploaded file | RAG |
|---|---|---|---|
| Knows your documents | No | Only uploaded (a little) | Large database |
| Sam chooses files | No | No | Yes |
| Source citations | No | Partially | Yes |
| Persistent base between sessions | No | No | Yes |
| Monthly cost (order of magnitude) | low | low | higher + implementation |
When RAG makes sense – and when it doesn't
My sense is that:
- Just a lot documents and the real cost of time spent searching;
- Many people use the Same knowledge;
- need audit trails and file references;
- The content is more or less okay established (contracts, procedures), not just chaotic, outdated notes.
It doesn't make sense (yet) when:
- You have a dozen files – paste them into the chat faster;
- you mainly need eemails, translations, brainstorming - a simpler tool is often enough; see our offer, where we combine carmation with customer processses;
- Nobody cares about the currency of documents in the index;
- one person Rarely sought – difficult to recoup the investment.
What RAG won't do (so you're not disappointed)
- It will not replace an expert – nadal you or a specialist verify, especially in regulated areas.
- It doesn't „think” like a human – searching and answers are made up of excerpts; if something isn't in the database, it won't „add it from memory” correctly.
- Quality = quality of the base and configuration.
- Hallucinations are rarer than with plain chat, but possible – hence Annotations are key.
How much does it cost (roughly)?
| Element | Fork |
|---|---|
| Implementation (platform / lightweight scope) | 1,000–5,000 EUR |
| Implementation (dedicated solution) | often 15,000–60,000+ EUR |
| Monthly infrastructure | hundreds of EUR (model, database, hosting in the EU) |
| Maintenance | depending on the SLA and complexity |
Return: when the team really saves many hours per week – calculate hourly rate × time. With a limited scope implementation, the approach helps MVP.
How to start reasonably
- Describe the problem with numbers – How much time is spent searching, how many people, what errors result from this.
- Document inventory – how much, where, in what condition.
- Who is supposed to see what – without this, you don't design for secure RAG.
- Pilot – one team, limited set of files, then scale.
When customers have Repeating questions before purchase, part of the „knowledge base response” logsc can also be considered on the online configurators and presentations – this does not replace internal RAG, but it can be a complement to the funnel.
Summary
RAG = Retrieval from your documents + Model response with source citations.
This is engineering, not a slogan. Implemented well – it saves time and reduces the risk of „guessing.” Implemented poorly – frustration and a wasted budget.
Would you like to see RAG with an example using documents from your industry? Schedule a consultation or write – without the obligation of a technical dictionary.
