RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation): RAG (Retrieval-Augmented Generation) is a technique where an AI system first searches your documents or databases for relevant information, then generates its answer using only that content instead of relying on what the model memorized.
RAG stands for Retrieval-Augmented Generation. It is the technique most business assistants use to answer with their own information: before replying, the system searches your documents for the passages on the topic and hands them to the language model together with the question. The model writes the answer from those passages.
Why do you need RAG?
A large language model knows what was in its training data, which is public and has a cutoff date. It does not know your prices, your returns policy or the status of a customer’s order. If you ask, it may return something that sounds credible and is false.
RAG solves that without touching the model. On every question, you give it the exact information it needs.
How does RAG work step by step?
- Preparation: the company documents are split into chunks and stored in a search index. Systems usually use semantic search with embeddings, which finds text by meaning and not only by exact words.
- Search: when a question arrives, the system retrieves the closest chunks.
- Generation: the model receives the question, the chunks and some instructions (“answer only with this information; if it is not there, say so”).
- Answer: the user gets the reply and, if configured that way, the source it came from.
Quality depends mostly on step 1. If documents are outdated, duplicated or contradict each other, the assistant answers badly even with an excellent model.
What does a business use RAG for?
RAG sits behind almost any assistant that answers from company data:
- Customer service: a web or WhatsApp chatbot that replies with the real terms of the service. It is the base of AI customer service.
- Internal assistant: your team asks about procedures, agreements or technical sheets and gets the answer with the source document.
- Technical support: search manuals and resolved tickets before escalating.
- Document review: find clauses in contracts or data in long case files.
Example: a distributor with 2,000 SKUs
Picture an electrical supplies distributor with a catalog of 2,000 products and technical sheets in PDF. Sales reps get questions every day like “can this cable go outdoors?” or “what do you have as an alternative to this out-of-stock item?”, and they take a while to find the sheet.
The company builds an assistant with RAG over the technical sheets and the stock table. The rep types the question and the assistant returns the answer, the item number and a link to the PDF where it read it. If the sheet says nothing about outdoor use, the assistant says so instead of guessing.
When a product changes, replacing its sheet is enough: the index updates and the assistant answers with the new version. Nothing needs retraining.
How do RAG, agents and other techniques fit together?
RAG is usually one piece inside something bigger. An AI agent uses RAG to know what to answer and tools to act: check the order, open a ticket or book a visit.
It is also not the only way to give context. If the information fits entirely in the conversation (a two-page policy), including it in the instructions is sometimes enough. RAG pays off when the volume of documents is large or changes often.
If you want an assistant that answers from your company documents, we build it within our AI agent development service, including cleaning and structuring the information.