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.

2023年12月5日 · RAG is a framework for improving model performance by augmenting prompts with relevant data outside the foundational model, grounding LLM responses on real, trustworthy information.

Retrieval Augmented Generation (RAG) is a technique used to augment a large language model (LLM) with external data, such as a company's internal documents. This provides the model with the context it needs to produce accurate and useful output for your specific use case.

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.

2023年12月13日 · In this article, we discuss various technical considerations when implementing RAG, exploring the concepts of chunking, query augmentation, hierarchies, multi-hop reasoning, and knowledge graphs....

2024年11月12日 · RAG is a method that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. It works by: Retrieval: When a user query is received, the system searches a large, up-to-date database or corpus for relevant documents.

3 天之前 · This article outlines and defines various practices used across the RAG pipeline—full-text search, vector search, chunking, hybrid search, query rewriting, and re-ranking. What is full-text search? Full-text search is the process of searching the entire document or dataset, rather than just indexing and searching specific fields or metadata ...

What is Retrieval Augmented Generation (RAG), and why is it valuable for GPT builders? RAG is the process of retrieving relevant contextual information from a data source and passing that information to a large language model alongside the user’s prompt.

Agentic RAG: Agentic RAG goes beyond traditional retrieval and generation by incorporating autonomous reasoning, decision-making, and iterative refinement into the retrieval process. Unlike standard RAG, which passively retrieves and generates responses, Agentic RAG leverages AI agents to actively engage with data, refine queries, validate ...

2024年10月30日 · Retrieval-augmented generation, or RAG, is a process applied to LLMs to make their outputs more relevant in specific contexts. RAG allows LLMs to access and reference information outside the LLMs own training data, such as an organization’s specific knowledge base, before generating a response—and, crucially, with citations included.