Retrieval-Augmented Generation RAG explanation

Retrieval-Augmented Generation RAG explanation describes the comprehensive understanding of how RAG systems enhance large language models by integrating external knowledge retrieval mechanisms into the generation process. This explanation covers the three-phase RAG workflow: retrieval phase where relevant documents are identified through semantic search, augmentation phase where retrieved context is processed and formatted, and generation phase where the language model produces responses grounded in external knowledge. The explanation emphasizes how RAG addresses hallucination problems by providing factual grounding, enables access to up-to-date information beyond training data limitations, and allows domain-specific customization without expensive model retraining. Key components include vector databases for semantic search, embedding models for document representation, retrieval algorithms for context selection, and prompt engineering techniques for effective context integration with generative models.