What is RAG in artificial intelligence and how to use it in your company

RAG explained with an analogy: the open-book exam

A language model is like a very bright student who has read half the Internet but has not seen your contracts, your procedures or your customers. If you ask about them, they improvise. RAG turns the exam into an open-book one: before answering, the system looks up the relevant pages in your documents and puts them in front of the student. The student writes the answer with them and tells you where each fact came from.

It does not require "retraining" the model. If you change a procedure, you update the document and the next answer already reflects it.

How RAG works, step by step

1. Ingestion: documents are collected (PDF, Word, wikis, emails, tickets) and their text extracted, with OCR if they are scanned.
2. Chunking: each document is split into meaningful chunks (sections, clauses) and metadata is added (date, area, permissions).
3. Indexing: each chunk is converted into a numerical vector (embedding) that represents its meaning and stored in a vector database.
4. Retrieval: given a question, the system looks for the chunks closest in meaning (and by keyword), filters by permissions and re-ranks them.
5. Generation: the model receives the question, the chunks and some instructions ("answer only with this and cite the source") and writes the answer.
6. Verification: sources are shown and, if there is not enough evidence, the system admits it instead of inventing.

RAG, fine-tuning or a long prompt: when to use each

In company projects RAG is the norm, sometimes combined with fine-tuning for tone or output format.

When a RAG project makes sense

Typical mistakes when building a RAG

• Indexing documents without cleaning them: duplicate and contradictory versions produce contradictory answers.
• Chunking by fixed size and cutting a clause in half.
• Using only semantic search and failing on exact references (codes, amounts, names).
• Not filtering by permissions and showing information to people who should not see it.
• Not evaluating: without a bank of questions with expected answers you do not know whether it improves or gets worse.
• Forgetting maintenance: documents change and the index must be updated.

How to start in your company

The safest way is a proof of concept with a single data source and 30-50 real questions written by the people who will use the system. In a few weeks you will know whether the quality is sufficient and what preparation your documents need. If you want us to do it, see our AI assistant over your company documents service; if the system must also carry out actions, we take it to an AI agent, and for customer service we expose it through a chatbot. Costs are in this price guide.

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