What is RAG? - Retrieval-Augmented Generation AI Explained - AWS
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.
What is Retrieval-Augmented Generation (RAG) - GeeksforGeeks
2024年6月11日 · Retrieval-augmented generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text.
What is retrieval-augmented generation (RAG)? - IBM Research
2023年8月22日 · 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.
Retrieval-augmented generation - Wikipedia
Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn ...
RAG - Hugging Face
Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate outputs.
What Is Retrieval-Augmented Generation, aka RAG? - NVIDIA Blog
2023年11月15日 · Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. In other words, it fills a gap in how LLMs work. Under the hood, LLMs are neural networks, typically measured by how many parameters they contain.
RAG and generative AI - Azure AI Search | Microsoft Learn
Retrieval Augmented Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding data.
What is Retrieval-Augmented Generation (RAG)?
What is Retrieval-Augmented Generation (RAG)? RAG (Retrieval-Augmented Generation) is an AI framework that combines the strengths of traditional information retrieval systems (such as...
What is Retrieval Augmented Generation (RAG)? - DataCamp
2024年1月30日 · RAG, or Retrieval Augmented Generation, is a technique that combines the capabilities of a pre-trained large language model with an external data source. This approach combines the generative power of LLMs like GPT-3 or GPT-4 with the precision of specialized data search mechanisms, resulting in a system that can offer nuanced responses.
RAG Evolution - A Primer to Agentic RAG - Unite.AI
1 天前 · What is RAG (Retrieval-Augmented Generation)? Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of large language models (LLMs) with external data retrieval to improve the quality and relevance of generated responses. Traditional LLMs use their pre-trained knowledge bases, whereas RAG pipelines will query external databases or documents in runtime and retrieve ...
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