Retrieval Augmented Generation (RAG) is an AI framework that retrieves facts and context from an external KnowledgeBase to ground large language models (LLMs) on accurate and up-to-date information. RAG faces challenges in effectively retrieving relevant information and generating high-quality responses. A ready-made RAG solution called RAGStack has been introduced to simplify the implementation process. Optimization techniques and addressing challenges in naive RAG systems are being explored. The RAG architecture overcomes the input length limit and knowledge cutoff problem.
Retrieval-augmented generation (RAG) architecture explained. RAG combines two main components: https://t.co/uWN3GXYWUJ
In this series of blog posts/videos, @sophiamyang walks through advanced RAG (Retrieval-Augmented Generation) techniques aiming at optimizing the RAG workflow and addressing the challenges in naive RAG systems: https://t.co/QqJsq63xsw
RAG faces a lot of challenges when it comes to effectively retrieving relevant information and generating high-quality responses. How can we improve RAG? 🎥https://t.co/48MXTwJbn6 One specific issue is that using the same big text chunk for retrieval and synthesis is not optimal… https://t.co/bIuMK7fnD3 https://t.co/yGrncUw4NT
The RAG architecture is quite efficient in overcoming the LLM input length limit and the knowledge cutoff problem. While RAG applications are easy to demo, they are difficult to put into production. Here's the breakdown of how to optimize your RAG Application: https://t.co/PYtb7BjQzO
RAG Retrieval-Augmented Generation = an #AI framework for retrieving facts & context from an external #KnowledgeBase to ground large language models #LLMs on the most accurate up-to-date information and to give users insight into the #GenerativeAI process: https://t.co/I2W5d5BLhJ https://t.co/AflYdijZIJ
📣 Production-ready RAG! Introducing RAGStack: a ready-made Retrieval Augmented Generation solution that makes implementing RAG simple and streamlined. Learn more: https://t.co/T1sqmR4fjw #RAG #GenAI #LangChain
Evaluations for Retrieval Augmented Generation: TruLens + Milvus https://t.co/jw1Sqxharp @zilliz_universe #AI #DataScience #LLMs #Database #VectorSearch https://t.co/p8PtChlaKm
A Beginner's Guide to Retrieval Augmented Generation (RAG) https://t.co/CGlL7nauEp via @SingleStoreDB by @Pavan_Belagatti