What is RAG and how can an AI respond using your documents?

Published on 2026-10-20 by frfrand

RAG explicado con ejemplos: cómo una IA busca información en documentos, qué son embeddings, por qué cita fuentes y dónde puede equivocarse.

RAG is a technique that combines information retrieval with response generation through artificial intelligence. Its acronym stands for Retrieval-Augmented Generation: generation augmented through retrieval. Instead of asking a model to respond solely based on what it learned during training, relevant information is first searched for and provided as context.

Imagine you have one hundred internal company manuals. A person asks "how can I request vacation?". A general model might invent a policy or answer with rules from another company. A RAG system attempts to find the correct section of your manuals and respond based on it.

The librarian example

Think of a library. The librarian does not need to memorize all the books. It is enough for them to know how to locate the indicated chapter, read it, and explain the answer.

With RAG something similar happens, although automated: first document fragments are retrieved and then a model builds the response. It is not the same as training the model from scratch with all the files.

What steps does a RAG system follow?

A typical flow has several stages:

1. Preparation: documents are loaded, their content is extracted, and they are divided into manageable fragments.
2. Indexing: those fragments are stored with useful information, sometimes converting them into vectors called embeddings.
3. Question: a person asks something in natural language.
4. Retrieval: the system identifies the most related fragments.
5. Generation: the model receives the question and the retrieved fragments to draft a response.

Not all systems use exactly the same search. Some combine keyword search with semantic search and then re-rank the results.

What are embeddings?

They are numerical representations of information used to compare similarities. Two sentences can express the same idea with different words: "how do I request days off?" and "procedure to request vacation".

A literal search may not easily detect the relationship. A semantic representation can help find related fragments by meaning.

But an embedding is not perfect knowledge. It can find a similar paragraph even if it is outdated, irrelevant, or contradictory. That is why it is important to combine it with filters, dates, permissions, and evaluation of results.

Does RAG eliminate hallucinations?

No. It can reduce invented responses when it retrieves appropriate sources, but the model can still misinterpret a fragment, omit a condition, or respond with excessive confidence.

If the internal policy says "only requests approved ten days in advance," the system should not respond "yes, you can request them tomorrow" without checking that condition.

A good design allows showing the sources consulted and supports responding "I did not find sufficient information" when appropriate.

The permissions problem

Imagine that the same system contains public documents, client contracts, and employee salaries. It is not enough for the search to find something relevant: it must verify whether that person is authorized to view it.

Control should not depend on politely asking the model to ignore private documents. Permissions must be applied in data retrieval and access.

There may also be documents with malicious instructions intended to deceive the model. Those fragments should be treated as untrusted information, not as authoritative orders.

When does it make sense to use it?

A support assistant over updated manuals, conversational search in technical documentation, or an internal FAQ system are good candidates. RAG is less useful if the problem only requires querying an exact number from a database via a conventional API.

Sometimes the most reliable solution is an SQL query or a defined function, not a semantic search.

What maintenance does it need?

If documents change, it is advisable to update the index. If a policy expires, it must be removed or its date indicated. Tests with real questions and an error log are also needed to improve retrieval.

Measure whether the retrieved fragments are relevant before blaming the model for an incorrect answer.

Where does ArduMaker fit in?

A RAG architecture can run with components hosted on VPS, databases, and AI services. In ArduMaker you can manage part of that infrastructure and connect applications, but retrieval, permission control, and source quality must be designed in your project.

In summary: RAG allows an AI to consult external information before responding. Its usefulness depends as much on the search engine and the data as on the model itself.

Sources: RAG system security according to OWASP · Introduction to OpenAI embeddings.