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Jun 11, 2024 · RAG, or retrieval-augmented generation, combines retrieval models that extract data from vast knowledge repositories with generative models that formulate pertinent responses. Learn how RAG improves accuracy, relevance, and versatility in natural language processing tasks, such as question answering, chatbots, and content generation.
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
Dec 13, 2023 · If you’re looking for a non-technical introduction to RAG, including answers to various getting-started questions and a discussion of relevant use-cases, check out our breakdown of RAG here.
- Chia Jeng Yang
Jun 24, 2024 · Retrieval Augmented Generation (RAG) is a transformative concept in AI and NLP. Additionally, by harmonizing retrieval and generation components, RAG addresses the limitations of existing language models and paves the way for more intelligent and context-aware AI interactions.
Aug 22, 2023 · Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information.
RAG definition: 1. a torn piece of old cloth: 2. clothes that are old and torn: 3. a newspaper or magazine that…. Learn more.
RAG meaning: 1. a torn piece of old cloth: 2. clothes that are old and torn: 3. a newspaper or magazine that…. Learn more.
