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.

Feb 10, 2025 · RAG, or Retrieval-Augmented Generation, represents a groundbreaking approach in the realm of natural language processing (NLP). By combining the strengths of retrieval and generative models, RAG delivers detailed and accurate responses to user queries.

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating an information-retrieval mechanism that allows models to access and utilize additional data beyond their original training set.

Jan 31, 2025 · Retrieval-augmented generation is a technique for enhancing the accuracy and reliability of generative AI models with information fetched from specific and relevant data 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.

Oct 30, 2024 · Retrieval-augmented generation, or RAG, is a process applied to large language models to make their outputs more relevant for the end user. A golden outline of a speech bubble is filled with a jumble of colorful, balloon-like spheres.

2 days ago · This hybrid model architecture is called Retrieval Augmented Generation, or RAG for short. ... if we extend our definition of retrieval to also encompass the ability to navigate and elucidate concepts previously unknown or unencountered by the model—a capacity akin to how humans research and retrieve information—our findings imply that DPR ...

Apr 10, 2025 · The hybrid model RAG (Retrieval-Augmented Generation) bridges retrieval systems and generative models to generate responses. The system allows AI to retrieve appropriate external information, which it then uses to create context-specific accurate responses. RAG models represent an improved approach over traditional systems because they use a ...

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.

Jan 17, 2025 · Retrieval augmented generation (RAG) has been the most talked about approach in mitigating the hallucinations faced by large language models. Today we will show you how the RAG approach works.

Mar 21, 2025 · A key benefit of RAG is that it gives the GenAI application access to domain-specific knowledge without retraining the entire model. At its core, RAG combines three essential elements: retrieval, augmentation, and generation. Imagine your …