Retrieval-Augmented Generation (RAG) is a trending concept in the field of Generative AI and Large Language Models (LLMs). It involves using LLMs to generate responses and retrieve information from a knowledge base for more accurate answers. Various companies and experts are exploring and utilizing RAG to enhance AI applications, with a focus on efficiency and accuracy.
Presenting danGPT: I pulled every post *ever* from @dan_abramov2 and built a RAG-based GenAI. It’s a silly side project that may be helpful. Let me know if it is, and if you’d like it open sourced and taught in a YouTube video or similar. 👉 Try it: https://t.co/MHVXWyXxDw https://t.co/GQ5vRcAeFz
If you’ve ever explored creating an AI-driven app, one concept you’ll come across is RAG (Retrieval-Augmented Generation). Would anyone be interested in a short explainer video about what it is exactly?
🐇Went on a rabbit hole to learn more about Retrieval-Augmented Generation (RAG) after reading about it for the first time in Elastic’s earnings call and a friend mentioning it to me yesterday, on RAG being used as a growing trend in LLMs. 🔴 Problem with GenAI and LLMs:… https://t.co/5fxMOG108M https://t.co/DqjH6wI0Iy
This article helps you understand #VectorDatabases and then dives into how they work with #LLMs, and #GenAI. via @KDNuggets https://t.co/uwbGiEJKhJ
Have you tried our Custom Prompt Engine to take your Retrieval Augmented Generation (RAG) system to the next level? What amazing things have you created with it? 👇 #AI #LLM #CustomPromptEngine https://t.co/VSwL4abrvQ
🎙️Roie Schwaber-Cohen, Staff Developer Advocate at @pinecone, joins Ben and Ryan to break down what retrieval-augmented generation (RAG) is and why the concept is central to AI in part one of a two part conversation. https://t.co/2ojcckkPQZ https://t.co/bq5EtIZVkp
2024's hottest skill: Learn how to build Retrieval Augmented Generation (RAG) applications using Large Language Models. RAG applications use an LLM to generate responses and retrieve information from a knowledge base to improve the accuracy of the answers. Most companies out… https://t.co/q8nf4wm8zC
Retrieval-Augmented Generation for AI-Generated Content: A Survey Comprehensively reviews RAG foundations, enhancements, applications, benchmarks, limitations, and future directions. 📝https://t.co/D78vdbmJoC 👨🏽💻https://t.co/Q4Rr0oudkk https://t.co/pgLJ5N68VW
Everything you need to learn about retrieval augmented generation is here in #RAGcheatsheet Learn in detail about #RAG here: https://t.co/Wwv5RNLRwm Credits: @Eduardo Ordax #LLM #languagemodels https://t.co/VwfbGcUApq
Build production-ready GenAI apps fast. #DataStax #VectorDB #RAG https://t.co/y9xFCMZAfO
Thanks to everyone who came to the @zilliz_universe webinar! The slides from my talk, Advanced RAG Applications with LlamaIndex, are here: https://t.co/VINA4w6hPr
Learn the secret to optimizing #GenAI workflows with your own data! 👀 @CBergman will give a Python demo on building a customizable #RAG stack using #Milvus, @LangChainAI, #Ragas, @HuggingFace, and optional #Zilliz Cloud and @OpenAI. Save your spot! https://t.co/5twTNBuMbG
I was happy to participate at @VectorInst's RAG Bootcamp event today, where I presented a notebook for building a Basic RAG with the @llama_index framework. Sharing the materials from that talk more broadly now in case there are others that could also benefit from them. (1/2) https://t.co/i3Vy8bd2Wg
Download the full 5-page PDF => RAG (Retrieval Augmented Generation) Cheatsheet (with links to GitHub and research papers): https://t.co/njdvcvmzsN ————— #AI #GenerativeAI #DeepLearning #Semantic #KnowledgeGraphs #GraphDB #VectorDB #LLMs #MachineLearning #DataScience https://t.co/eOxf2auAoT
Dive into the future of GenAI with RAG & end outdated results & long prompts. Discover how Retrieval-Augmented Generation can supercharge LLM projects. Perfect for those seeking accuracy & efficiency. https://t.co/Amya3BV31F #GenerativeAI #DataScience https://t.co/tFW5DAJeGo
Introducing RAG Networks 🔎🌐 We’re excited to release `llama-index-networks`, allowing users to supply and consume data sources through a network exchange. That way, data suppliers can allow access to their data sources through a RAG endpoint. Data consumers no longer have to… https://t.co/YTbVxLCPuG https://t.co/KBSde83KT1
✨Brand new feature:✨ combine RAG applications into a distributed super-RAG! Our new llama-index-networks feature lets you ➡️ create an API service for any RAG application ➡️ connect RAG apps into a single network ➡️ effortlessly run queries across the entire network ➡️ get a…
The rise of #LLMs has made vector embeddings and #VectorDB immensely popular and useful, particularly in #AI and #GenerativeAI applications. 🌟💯🌟 @bindureddy from @abacusai explains in this “RAG - Vector Retrieval - Comprehensive Study”... ⬇️ ⬇️ https://t.co/dIzKtFBzXv
Retrieval Augmented Generation (RAG) clearly explained: https://t.co/2hqPhmF1Se