Retrieval-augmented Generation (RAG) is an AI approach that improves machine understanding and response accuracy. By integrating traditional AI language models with real-time retrieval of relevant external data, RAG bridges knowledge gaps, enabling more precise and contextually rich answers. By Vidhi Gupta.
This article introduces Retrieval Augmented Generation (RAG), a powerful technique combining Large Language Models (LLMs) with external data retrieval. Unlike static LLMs that can hallucinate or provide outdated info, RAG dynamically pulls relevant information from trusted sources before generating responses.
Key benefits include: * Improved Accuracy: Reduces errors (“hallucinations”) by grounding answers in verified data. * Real-Time Data: Ensures responses use the most current knowledge available. * Enhanced Context: Leverages existing human-made content and expert knowledge bases for richer, more relevant outputs.
Common applications involve chatbots providing reliable customer support, summarizing research (e.g., legal or medical), translating languages accurately based on domain context, and personal assistants handling complex tasks using integrated information. Nice one!
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