Retrieval Augmented Generation (RAG) is gaining prominence in the AI community as a technique that combines large language models (LLMs) with real-time data retrieval to enhance decision-making and information generation. RAG agents are being used for efficient data retrieval and decision-making in real-time applications. Businesses are leveraging RAG to unlock domain-specific, real-time data for generative AI, enhancing reliability and productivity. Various experts and companies are exploring the potential of RAG in different applications, such as vector embeddings and setting up RAG pipelines with real-time analytics.
📽️How to build agentic #RAG with llama3? https://t.co/fFB8BHMmJe 1⃣Better data parser 2⃣Chunk size 3⃣Rerank 4⃣Hybrid search 5⃣Agentic RAG 👍Great video from @jasonzhou1993
🤖✨ Say “Yes” to Retrieval-Augmented Generation! 🎉 RAG blends real-time data and AI for ultra-smart, accurate answers. Find out more. #RAGTech #SmartAI #Innovation #AIChatbots 🚀🔍 https://t.co/Z8t3jPTOwp
Imagine a large language model taking an open-book test. 📚 This is similar to how Retrieval Augmented Generation (RAG) works. #RAG integrates the generative abilities of LLMs with data retrieved from specified sources. Discover how this combination ensures information remains… https://t.co/64fNyneQmy
In enterprise-grade #AI, Retrieval-Augmented Generation (RAG) is key: Database data + generative #LLMs creates highly relevant, contextually rich responses. #RAG enhances depth, accuracy of AI outputs: Prompt -> database -> LLM => Output Responses more accurate + insightful. https://t.co/zhP56geX7C
🔥 RAG is the talk of the town in AI project! 👩💻Don't miss our blog for a beginner's rundown on: - What is RAG - RAG architecture - How RAG works - Challenges in building RAG systems - RAG's application scenarios Read now: https://t.co/r2020YASot #AI #RAG #MyScale
Doing RAG? Vector search is *not* enough! https://t.co/Sq227a927c I've updated my post with evaluations I ran on one of my RAG apps: https://t.co/I3dpvt4ctw
RAG isn’t as easy as embedding search++ :) https://t.co/Yi3WuKDlfQ
🙌🏼 RAG++ Hack Night 🙌🏼 Join our high-energy hack party featuring top RAG (Retrieval Augmented Generation) technology experts in the industry 🧠 ⌨️ Team up with DataStax, @UnstructuredIO, @LangChainAI and @Voyage_AI_ to connect with RAG professionals, witness AI production… https://t.co/Ojqnzwjtho
The 4 Advanced #RAG Algorithms You Must Know to Implement by @iusztinpaul https://t.co/NF1pZxAjlZ
Retrieval augmented generation, or RAG, has emerged as a popular technique to fine-tune LLM models. However, when it comes to RAG systems, it's important to pay attention to how you break up your data. We discuss some strategies and what works best: https://t.co/aL5qHxhb2H https://t.co/vuOhHGWGoe
Evaluating Your RAG Systems with These Metrics! Evaluating Retrieval Augmented Generation (RAG) systems can be tricky, but breaking it down into smaller tasks helps. The retrieval part is well-researched, and there are some great metrics to check out. @helloiamleonie wrote an… https://t.co/Ev6YpZHqfh
🤖🇺🇸 Is RAG the Future of AI? Discover how Retrieval-Augmented Generation (RAG) could transform generative AI by eliminating "hallucinations" and enhancing reliability and productivity. A game-changer for businesses relying on AI! https://t.co/IXaisCIRjG
Businesses can unlock domain-specific, real-time data for generative AI through #RAG. Download the e-book to learn how to set up workflows for building #AI assistants and chatbots. https://t.co/idTgqC4rxF https://t.co/uEYOBrVQzd
👨🏻💻 Demo alert! 👩🏽💻 Discover how to build an AI application that uses RAG (Retrieval Augmented Generation) without the need for coding — and get it running in minutes. Watch the @TechWithTimm video ⬇️ https://t.co/gfY2McxI5u #Langflow #RAGApplications #DeveloperCommunity
📹 This webinar is perfect for intro-level professionals looking to understand the basics and applications of RAG Agents. Register today: https://t.co/o7se5GylQP #RAG #AI #webinar #HowTo #SingleStore
great new article on optimizing LLMs for accuracy, answering questions like when to just prompt engineer vs when to fine-tune or use RAG (or both) https://t.co/O3KBzeF30P
🔥 Happening Now: Led by @MathesonZander , @LGFunderburk, @Nina_Lopatina_ , and Shagun Sharma, learning to set up RAG pipelines with real-time analytics, integrate Bytewax, Azure AI (@Microsoft ), and @UnstructuredIO tools. 👀 Can't make it now? Sign up for the recording.… https://t.co/3kcwXSYeRz
Optimizing LLMs for accuracy can be challenging. @colintjarvis has distilled a year’s worth of insights into a new practical guide. Discover how to start optimizing, choose the right methods, and achieve production-ready accuracy: https://t.co/G4pmJE16jd https://t.co/dgOFPZ5UdM
🤖 From this week's issue: Fine-tuning is the process of taking a pre-trained LLM and adapting it to a specific task or domain. https://t.co/XYxVQsWcNs
i have generally been down on RAG as ive found most of the implementations terribly naive. was forced to put together a few pipelines for demos: smart semantic chunking + scann + reranking + BM25 is actually pretty fucking solid even for moderately complex data.
Excited to talk about vector embeddings and how state of the art RAG applications use @weaviate_io! https://t.co/8Jg8RKeJSX
Make room for RAG: How Gen AI's balance of power is shifting AI's RAG is not a panacea for hallucinations, but it's here to stay, which may mean ultimately adapting the training or large language models to better accommodate RAG. https://t.co/ovZbdllJMF @OpenAI @elastic…
I've been pretty confused about what RAG for LLMs is; so this was a great post by my coworker that helped me understand it better, both the idea and practice. Basically, fancy search to find relevant documents you concatenate as text to a prompt. But the devil's in the deets. https://t.co/HCXZFV7re4
💡 This webinar will explore how the synergy between these technologies enables the creation and management of RAG agents, which are essential for efficient data retrieval and decision-making in real-time. Register: https://t.co/bivrPIHe79 #AI #data #RAG #SingleStore