What Is RAG: How to Make AI Use Your Company's Data

You ask ChatGPT how much your flagship product costs and it confidently invents a number: that's exactly the problem RAG solves. RAG (Retrieval-Augmented Generation) is a technique that connects an AI model to your company's documents: for every question, the system first searches your information for the relevant fragments, and then the model writes the answer using that real data, citing the source. Nothing has to be trained: the model stays the same, but now it answers with your prices, your manuals, and your policies.

Why ChatGPT doesn't know your company

A model like GPT or Claude is trained once on a giant corpus of public text. After that, its knowledge is frozen. That means:

• It doesn't know your data: your price list, your return policy, and your onboarding manual were never part of its training.
• It doesn't update with your operation: if you change a rate tomorrow, the model has no idea.
• And worst of all: it doesn't know that it doesn't know. If you ask it something about your company, it will answer anyway, in a confident tone, making things up. This is what's called a "hallucination," and it's the number one reason companies distrust putting AI in front of customers.

The two possible solutions are to train the model with your data (expensive and rigid) or to feed it the right information at the moment of the question (RAG). Spoiler: in 2026, for 95% of companies, the right answer is the second one.

How RAG works, without the jargon

The full flow has four steps. Let's follow a 40-page warranty manual:

1. Ingestion: the document is split into manageable fragments (paragraphs or sections).
2. Embeddings: each fragment is turned into a vector — a list of numbers that captures its meaning. Fragments about the same topic end up "close" to each other, even if they use different words. This lets the search understand that "do you cover a cracked screen?" relates to the section "accidental display damage."
3. Search: when someone asks a question, the system converts the question into a vector and retrieves the 3-5 fragments closest in meaning.
4. Generation with sources: those fragments are passed to the model along with the question and a key instruction: "answer using only this information." The model writes the answer and the system shows which document it came from.

The result: an AI that answers "according to the warranty policy (section 4.2), screen damage is covered for the first 6 months" instead of inventing. And if you also want that AI to take actions (generate the warranty ticket, schedule the service), that's already the territory of AI agents — RAG is usually one of their building blocks.

The myth of "training your own AI"

A phrase we hear often: "we want to train an AI with our data." In 95% of cases, what the company needs is RAG, not training. The honest comparison:

Criterion	Fine-tuning (training)	RAG
Initial cost LATAM 2026	USD 20,000-100,000+	USD 3,000-10,000
Implementation time	3-6 months	3-6 weeks
Updating information	Re-train (weeks, USD thousands)	Upload the new document (minutes)
Answers with citable sources	No	Yes
Risk of inventing data	High (can't tell what it learned)	Low and controllable
When it's justified	Very specific tone/format, very high volume	Answering with company information

Fine-tuning is good for teaching a model how to speak (a style, a very particular format); it's not a good way to teach it what to know, because the data gets mixed into the model's weights, with no sources and outdated from day one. For company knowledge, RAG wins by a landslide on cost, maintenance, and verifiability.

Want your team or your customers to query your company's information in natural language? Book a 30-minute meeting and we'll show you a RAG system running on real documents.

Which documents work (and which will cause you trouble)

Work very well:

• Product manuals and technical documentation
• Internal policies: warranties, returns, HR, compliance
• Price lists and product sheets (better if they're structured)
• FAQs and good-quality historical support answers
• Operating procedures and onboarding material

Cause trouble:

• Contradictory versions of the same document: if the 2023 policy and the 2026 policy coexist, the system might retrieve either one. You have to clean up before ingesting.
• Giant unstructured spreadsheets: an 80-column matrix doesn't fragment well; it's better to transform it or connect it as structured data.
• Poor-quality scanned documents: modern OCR helps, but an unreadable PDF is still unreadable.
• Knowledge that isn't written down: RAG can't retrieve what lives only in your best employee's head. Sometimes the project starts by writing those 10 documents nobody ever wrote.

How much work is it to maintain

Less than people fear, if it's designed well. The real maintenance of a RAG knowledge base is:

• Updating documents when they change: upload the new version (re-ingestion is automatic). Effort: minutes per document.
• Reviewing unanswered questions: the system logs which queries found no information; that list tells you exactly which document is missing. Typical review: 1-2 hours per month.
• Auditing answers periodically: a monthly sample of conversations to catch drift.

The trap to avoid: treating it as a project that gets "delivered and that's it." A knowledge base is a living asset; with an internal owner who spends a couple of hours a month on it, it maintains itself. Without an owner, in a year you'll be answering with old prices.

How you stop the AI from inventing

The key question for any decision-maker, and it has a concrete technical answer:

1. Strict grounding: the model is instructed to answer only with the retrieved fragments. If the data isn't there, it doesn't answer.
2. Explicit "I don't know": when the search finds no relevant fragments, the system replies that it doesn't have that information and routes to a human — instead of improvising.
3. Visible sources: every answer cites the source document and section. What's verifiable is auditable.
4. Confidence thresholds: if the similarity between question and fragments is low, it escalates instead of taking a risk.

With these four layers, well-implemented RAG systems operate with error rates on the order of 1-5% depending on the domain — and, unlike a tired human at 7 PM on a Friday, they're audited in full.

When you DON'T need RAG

• If your queries are about live transactional data (stock, order status), the right move is to connect the AI directly to your system via AI integration, not to turn the database into documents.
• If you have 5 frequently asked questions and nothing more, an AI chatbot with those answers configured is enough and costs less.
• If your documentation is a mess of versions, the first project is to clean it up. RAG over garbage returns garbage with good wording.

What to do with this

If your team wastes hours hunting for "which PDF was that in" or answering the same internal question for the umpteenth time, RAG is one of the AI projects with the fastest return and lowest risk available today.

At Deepyze we implement RAG knowledge bases end to end: document cleanup, ingestion, integration with your channels (web, WhatsApp, intranet), and the anti-invention mechanisms described above. Fixed price, a team in your time zone, and a concrete proposal in 24 hours: tell us what information you want your AI to know.