Short answer: RAG (Retrieval-Augmented Generation) is a technique in which the AI first searches your documents and then answers based on what it finds, citing the source. It is the most common way to have an assistant that knows your company without retraining a model.
The problem it solves
A general-purpose language model does not know your manuals, your price lists or your contracts, and it can invent convincing-sounding answers. RAG reduces that risk by making it consult a controlled information source before answering.
How it works
- Ingestion: documents are collected (manuals, procedures, data sheets, FAQs).
- Chunking and indexing: they are split into pieces and stored in an index that allows searching by meaning.
- Retrieval: given a question, the system finds the most relevant chunks.
- Generation: the model writes the answer using those chunks and can indicate where they come from.
- Control: permissions, filters and logs are applied so you know who asked what.
Use cases
- Internal support: the team looks up procedures, policies or technical sheets.
- Customer service: answers based on your public documentation.
- Sales: searching product information and similar cases.
- Compliance: locating clauses and requirements in large sets of documents.
What determines quality
- Document quality: if there are contradictory versions, the system will be contradictory too.
- Structure: clear headings and well-divided content improve retrieval.
- Permissions: each person should see only what applies to them.
- Continuous evaluation: a set of test questions with expected answers.
- Updating: a process to add changes and retire obsolete documents.
RAG, fine-tuning or prompt: which to choose
| Option | When |
|---|---|
| Prompt with instructions | Style and simple rules, little proprietary information. |
| RAG | Answers based on documents that change and must be citable. |
| Fine-tuning | Changing the model’s behaviour or style systematically; not the route for loading knowledge that changes. |
Risks and how they are mitigated
- Answers without a source: require a citation and show “I couldn’t find information”.
- Information leaks: per-user access control and separate environments.
- Personal data: minimise, anonymise where possible and review the supplier.
Frequently asked questions
Do I need thousands of documents?
No. A well-organised set of dozens of documents can already be useful. Quality weighs more than volume.
Is my data used to train the model?
It depends on the provider and the contract. Check the terms and choose options that do not use your data for training.
Can it be hosted on my infrastructure?
It is possible with compatible models and components. It is assessed against security, cost and performance requirements.
At IGLOBALIA we deploy assistants with your data and access control. See the applied AI agency or let’s talk about your case.
