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.

3 天之前 · The companies that have tried to deploy RAG have learned the specifics of such an approach, starting with support for the various components that make up the RAG mechanism. ... a vector database ...

Retrieval-augmented generation (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. [1] 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 supplement information …

Learn about the components of a Retrieval Augmented Generation (RAG) architecture and learn about how the RAG approach can help you query custom documents.

2025年3月12日 · Simply put, a vector database is a specialized type of database optimized for the storage and retrieval of text represented as high-dimensional vectors. Why are these databases crucial for RAG? Because vector representations enable efficient similarity-based searches over large document bases, quickly retrieving relevant information based on a ...

2025年2月4日 · Organizations use retrieval augmented generation (or RAG) to incorporate current, domain-specific data into language model-based applications without extensive fine-tuning. 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.

2025年3月14日 · Retrieval Augmented Generation (RAG) is a technique that enhances LLMs by integrating them with external data sources. By combining the generative capabilities of models like GPT-4 with precise information retrieval mechanisms, RAG enables AI systems to produce more accurate and contextually relevant responses.

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.

2025年1月21日 · Top vector databases optimized for Retrieval-Augmented Generation (RAG). Learn why RAG relies on vector databases and explore short code examples to integrate each.

2025年1月17日 · New to the world of Retrieval Augmented Generation (RAG)? We've got you covered with this in-depth guide where you'll learn what RAG is, the advantages and real-time use cases. We'll also walk you through RAG applications, and RAG using LangChain.