Le RAG (Retrieval-Augmented Generation), or increased generation of recoveryis an advanced AI technique that improves the quality and relevance of responses produced by language models by connecting the system to a knowledge base.
Rather than relying solely on the knowledge acquired during training, these models will search for information in real time in databases, documents or other external sources before generating a response. This allows them to be more precise, better informed and contextually more relevant.
🔧 How does the RAG work?
The RAG is based on a two-stage process :
- Information retrieval :
- When a question is asked, the model first searches for the most relevant documents or text passages in an external knowledge base (e.g. articles, databases, internal documents, online APIs).
- Generation :
- Once the information has been retrieved, the model uses it to formulate a response that integrates this data in a natural and coherent way.
💡 Example:
A RAG-based medical assistant can search recent medical databases (such as PubMed) before answering a question about a treatment. In this way, it does not limit itself to the fixed knowledge of its training, but also provides an answer based on the latest scientific research.
✅ Why use RAG?
The RAG has several major advantages :
✔ 📌 Improved precision
By relying on up-to-date sources, the RAG reduces the risk of "hallucination "(invention of facts) and produces answers more reliable and factual.
✔ 📖 Better contextualisation
It allows us to better understand the user's request and respond to it. with greater relevanceby integrating context-specific information.
✔ 🎯 Personalise your answers
The RAG can be adapted to the specific needs of a user or an industry (law, finance, health, e-commerce, etc.), by consulting specialised databases.
✔ 📡 Ability to keep up to date
Unlike conventional models, which cannot learn after training, the RAG can access recent information without having to completely reformat the model.
📌 Concrete examples of RAG use
💬 1. Chatbots intelligent
👉 A customer service department uses a RAG chatbot that consults a database of FAQs and technical manuals to provide precise answers and avoid unnecessary redirections to a human agent.
🔎 2. Advanced search engines
👉 A RAG-based legal search engine can retrieve and summarise relevant court decisions or articles of law to answer a lawyer's questions.
📝 3. Writing tools
👉 A journalist uses a RAG-based assistant that extracts up-to-date statistics and quotes to enrich his article.
🧠 4. Medical AI
👉 A doctor questions a RAG medical assistant who retrieves up-to-date information on the side effects of a new drug before giving advice to his patient.
🏢 5. In-company assistance
👉 An employee asks a question about their company's internal policies; the RAG AI consults internal documentation live and responds with relevant information.
