You ask your company's AI chatbot about the terms of your warranty policy. The chatbot generates an answer — confidently, fluently, grammatically flawless. And completely made up. This problem has a name: hallucination. And it has a solution: RAG. If you are thinking about AI that works with your actual data — internal documents, product catalogues, contracts, FAQs — this is the technology you need to understand first.
Why Generic AI Hallucinates Over Your Business Data
Large language models like GPT-4 or Claude are trained on enormous volumes of publicly available text. They know encyclopaedic facts, programming languages, textual patterns. But they do not know your company.
When you ask GPT about your internal invoice approval process, the model has no relevant information — yet it generates an answer anyway. A language model is a statistical machine: it predicts the most probable continuation of text. When facts are missing, it substitutes a plausible-sounding alternative. The result is text that sounds correct but the content is invented.
The common workaround — pasting your entire company document into the query — only works up to a certain size. Model context windows have limits. If your documentation runs to 500 pages, it will not fit in the context.
What RAG Is and How It Works Step by Step
RAG (Retrieval-Augmented Generation) solves this problem by combining two steps: first retrieve the relevant parts of your data, then pass them to the model as context for generating the answer.
The full process looks like this:
1. Indexing your data (done once, updated continuously)
Your documents — PDFs, Word files, database records, web pages — are split into smaller segments (chunks). Each chunk is converted into a numerical vector using an embedding model. This vector captures the semantic content of the text — not the exact words, but their meaning. The vectors are stored in a vector database.
2. Retrieving relevant chunks (on every query)
A user types a question. That question is also converted into a vector. The vector database finds the chunks whose vectors are mathematically closest to the question vector — the chunks with the most similar content.
3. Generating the answer
The retrieved chunks are passed to the language model alongside the original question. The model receives an instruction: answer only based on these materials. The result is an answer grounded in your actual data.
Practical tip: The quality of RAG depends 70% on the quality of your data and how it is split into chunks. Poorly structured documentation produces poor answers even with a technically correct implementation.
Real-World Examples
Real-world example: Czech insurance brokerage Kovářík & Partneři had a customer centre handling hundreds of queries daily about product terms from various insurers. Agents had to switch between dozens of PDF documents. After deploying a RAG chatbot over their product documents (3,400 pages in total), average query resolution time dropped from 4 minutes to 40 seconds. The chatbot always cites the exact document section the answer comes from.
Real-world example: Manufacturing firm Strojmetal Příbram implemented RAG over their technical documentation — machine manuals, service procedures, safety data sheets. Technicians on the floor can now instantly look up the procedure for a specific fault type via a mobile app, without searching through paper binders.
At BASAD Studios, we developed our own product LawyerAI — an AI legal assistant built on RAG architecture. LawyerAI lets lawyers and in-house legal teams query their own collection of contracts, court rulings, and regulations. The system answers with a precise citation of the source it draws from, which is critical in legal contexts. The model does not guess — it responds only when the relevant text actually exists in your documentation.
RAG vs. Fine-Tuning: Why RAG Wins for Business Data
The alternative to RAG is fine-tuning — retraining the model on your data. Why is RAG the better choice for most business applications?
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Implementation cost | Low to medium | High |
| Updating data | Immediate | Requires retraining |
| Answer traceability | Cites source | Not traceable |
| Hallucination risk | Low (grounded in data) | Higher |
| Best suited for | Documents, FAQs, catalogues | Style, tone of voice, domain jargon |
Fine-tuning makes sense when you want to teach the model a specific communication style or specialised terminology. For answering queries from company documentation, RAG is faster, cheaper, and safer.
A key RAG advantage: your data does not become part of the model. If you update prices in the catalogue, you re-index the catalogue. With fine-tuning you would have to retrain the entire model.
What You Need to Implement RAG
The technical architecture of RAG consists of three components:
Embedding model converts text into vectors. Examples: OpenAI text-embedding-3-large, Cohere Embed, or open-source models like nomic-embed-text. The choice depends on the language of your documents — for Central European languages, test the model against real samples before committing.
Vector database stores the vectors and enables fast retrieval of similar chunks. The most common options: Pinecone (cloud, easily scalable), Qdrant (open-source, self-hostable), pgvector (PostgreSQL extension, useful if you already run PostgreSQL).
Language model (LLM) generates the final answer. GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro — or open-source alternatives like Llama 3.3 for on-premise deployment.
You also need an orchestration layer — the code that connects these components, manages context, filters results, and ensures the model does not stray beyond the provided facts.
Practical tip: Start with the smallest reasonable dataset — one FAQ category or one product catalogue. Validate answer quality against real queries before indexing your entire company documentation.
Costs and Timeline for a Basic RAG Implementation
Indicative figures for a company with documentation up to 1,000 pages:
| Phase | Timeline | Cost |
|---|---|---|
| Data analysis and architecture design | 1–2 weeks | €600–1,200 |
| Implementation and indexing | 2–4 weeks | €1,600–3,200 |
| Testing and tuning | 1–2 weeks | €600–1,000 |
| Ongoing operating costs (API, hosting) | monthly | €80–320/month |
Costs vary significantly based on data volume and quality, integration requirements with existing systems, and whether you choose cloud or on-premise deployment.
Real-world example: Electronics e-shop ElektroPlus Praha implemented a RAG chatbot over their product catalogue (8,500 products with technical specifications). The chatbot answers customer technical questions and compares products. Total implementation took 6 weeks, monthly operating costs are around €180. Support contacts dropped by 28% for technical queries.
Data Security: What Happens to Your Documents
This is a legitimate concern, especially in regulated industries or with documents containing trade secrets.
Your data does not train OpenAI models. When you use the OpenAI API (not the ChatGPT web interface), documents sent as part of a query are not used for training — this has been the case for API access since 2023. The same applies to the Anthropic API and Google Cloud.
For sensitive data, there is the option of private deployment: an open-source LLM (Llama, Mistral) running on your own infrastructure or a private cloud. In this setup, data never leaves your environment at all. The trade-off is lower model performance compared to proprietary alternatives — but for many use cases, that difference is acceptable.
The vector database can also run on-premise — Qdrant or pgvector with no dependency on external cloud services.
Practical tip: Before implementing RAG, classify your documents. Documents containing commercially sensitive information may require different handling than public FAQs or general product information.
When RAG Makes Sense — and When It Does Not
RAG is the right choice when:
- You have extensive documentation that employees or customers find difficult to search
- You need answers grounded in specific company context, not general information
- Your data changes regularly and you need AI to respond immediately to updates
- You need traceability — knowing which part of which document an answer came from
RAG is not the right choice when:
- Your "documentation" is actually a 50-row spreadsheet — a simple database query is enough
- You need the model to perform actions (RAG only answers — for actions you need an agent architecture)
- The quality of your source documents is very low — garbage in, garbage out
At BASAD Studios, we build AI automation solutions including RAG implementations for businesses across industries. If you want to know whether RAG makes sense for your specific case, get in touch or check out our AI automation service.