RAG (Retrieval Augmented Generation)
An AI technique that enriches language models with your own data
Definition
RAG (Retrieval Augmented Generation) is a technique that enhances AI language models by first retrieving relevant information from your own data sources before generating a response.
Detailed explanation
RAG solves a key limitation of standard AI models: they only know what they were trained on. With RAG, the AI can access your internal documents, knowledge bases, and databases in real time.
This makes AI responses more accurate, up-to-date, and grounded in your company's actual data, rather than relying solely on the model's training data.
Real-world examples
Internal knowledge base
AI assistant that answers questions using your company documentation
Customer support
Chatbot that references your product docs and FAQ to answer queries
Why it matters
- AI grounded in your real data
- Always up-to-date responses
- Reduced AI hallucinations
- No need to fine-tune models
Related terms
Service associé
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