# RAG: making the company's knowledge truly usable

> An RAG system links an AI to trusted documents to produce useful, sourcing and rights-of-access responses.

AI applied

## RAG: making the company's knowledge truly usable

An RAG system links an AI to trusted documents to produce useful, sourcing and rights-of-access responses.

![actualite-rag.png](https://sdx-development.com/media/News/actualite-rag.webp?v=1788084423)

[Applied AI](https://sdx-development.com/en/news/applied-ai)Published on 10 July 2026 at 09:00

The RAG, for Retrieval- Augmented Generation, allows an AI assistant to search a documentary database before formulating his answer. Its value depends primarily on the quality of the information system it uses.

 

## Own and identified sources

 

An RAG does not correct obsolete or contradictory documentation. Reference sources should be identified, their updating cycles should be understood, and content that should not be interviewed should be excluded.

 

## Research before generation

 

The answer is good if the right extracts are found. Document cutting, metadata, search engine and rights filters are therefore structuring choices. AI should not respond with confidence when the source is missing.

 

## Traceable answers for teams

 

Displaying the documents used, the passages cited and the level of trust helps the user to verify the information. This is particularly important when the RAG assists a business team, support, quality or operations.

APPLIED AI

## Turn an AI prototype into a production workflow.

Computer vision, YOLO and RAG become useful when they connect to your real data, teams and constraints.

- Defined data and metrics
- Deployment connected to your tools
- Quality monitoring after launch

[Assess feasibility](https://sdx-development.com/en/contact)[Explore AI, RAG & computer vision](https://sdx-development.com/en/ai-rag-automation)

---

[View the HTML page](https://sdx-development.com/en/news/applied-ai/rag-making-company-knowledge-usable)