RAG (Retrieval Augmented Generation)
RAG
Description: RAG is a hybrid AI technique combining retrieval and generation to enhance LLM performance. Introduced by Facebook AI in 2020, RAG pairs a language model with a retriever that fetches relevant documents or data from an external knowledge base (e.g., Wikipedia, custom datasets) before generating responses. This reduces hallucinations and improves factual accuracy, making RAG ideal for question answering, research and knowledge intensive tasks. Its adoption has surged in 2024-2025 as organizations seek context aware AI solutions.
Key Features:
Reduces hallucinations in LLMs.
Uses external knowledge bases.
Use Cases: Question answering, fact checking.
