Retrieval Augmented Generation
RAG combines LLMs with vector search and retrieval systems. The model retrieves relevant context from a knowledge base before generating responses, reducing hallucinations and improving accuracy.
Why it exists
- •LLMs don't know your private data
- •LLMs hallucinate confidently
- •RAG bridges AI + real knowledge
Used in
What is RAG?
RAG is like giving an AI a library to look things up in before answering. Instead of just remembering, it can check real information first!
👶 For Beginners
RAG is like giving an AI a library to look things up in before answering. Instead of just remembering, it can check real information first!
👨💻 For Developers
RAG combines LLMs with vector search and retrieval systems. The model retrieves relevant context from a knowledge base before generating responses, reducing hallucinations and improving accuracy.
🚀 For Founders
RAG solves the hallucination problem by grounding AI responses in real data. Essential for building trustworthy AI products that need accurate, up-to-date information.