What is Retrieval-Augmented Generation (RAG)?

Empowering LLMs with Up-to-Date and Factual Knowledge

How RAG Combats Hallucinations and Improves Accuracy

Building More Reliable and Context-Aware AI Systems

**Retrieval-Augmented Generation (RAG)** is an AI architecture that significantly enhances the factual accuracy and relevance of **Large Language Models (LLMs)**. Instead of relying solely on the information the LLM learned during its initial training, RAG allows the model to **retrieve relevant information from an external, authoritative knowledge base** (like a company's internal documents, a specialized database, or the internet) *before* generating a response. This retrieved context is then used to inform the LLM's output, drastically reducing **hallucinations** and providing more up-to-date and specific answers. RAG is a key technique for building reliable and enterprise-ready LLM applications in 2025. 📚🔍