Several companies and organizations are leveraging Retrieval-Augmented Generation (RAG) technology to build chatbots and search applications. The technology combines language models (LLMs) with retrieval mechanisms to enhance information retrieval and organization. Companies such as Zilliz, LangChainAI, and LlamaIndex have introduced tools and platforms to facilitate the development of RAG applications. Additionally, there are discussions and case studies comparing RAG with fine-tuning for domain-specific applications, emphasizing the effectiveness and ease of implementing RAG. Bootcamps and workshops are being organized to educate developers on building RAG applications at scale, with experts from various companies sharing insights and presenting new code patterns.
Learn from the best how to build RAG 👇free bootcamp proudly presented by @anyscalecompute + @pinecone https://t.co/X26MlN1y0n
RAG vs. Fine-Tuning - A Case Study on Agriculture Our customers commonly ask us if we can fine-tune the LLM for their domain. Of course we can, but it depends on data! If you have several supervised examples, we can fine-tune a model. Additionally, RAG + fine-tuning will… https://t.co/KjXFBgrwK3
Really excited for this! Current talk title is: When Simple RAG Fails (and how to fix it) https://t.co/9AP005eYNR
Come learn to build RAG LLM apps at scale with a bunch of brilliant folks from @pinecone and @anyscalecompute. My team and I will be presenting some 🔥 new code patterns https://t.co/p936JqQhHL
I can't think of any LLM-powered app that does not have some RAG component to it. This is a great intensive 2-day boot-camp for folks who want to do RAG development at scale which is basically what is needed for today's applications. Register now!! https://t.co/OR2hkamAde
#Zilliz Cloud's latest update is crafted for intricate use cases like RAG/AI, recommendation systems, and cybersecurity/fraud detection—setting a benchmark in #VectorDatabase technology. @StarlordXie Learn about the real-world use cases: https://t.co/sV597XrJLg by @FrankZLiu https://t.co/yHOxg92PMi
This is the first hands-on, intensive, two-day bootcamp for learning to build RAG applications. Cohosted by @pinecone and @anyscalecompute (also featuring lessons from experts at @LangChainAI, @vercel, and others). Nearly every AI application will be a RAG application, and… https://t.co/Q25TYlOX8T
Building an enterprise #generativeAI app? You need more than just a good LLM—users want real-time, accurate data from your database. On January 31, learn how RAG bridges the gap—feeding data to LLMs for relevant, domain-specific responses → https://t.co/lNiWTPwJt8 https://t.co/9HAsRXN8pr
Ready to hear from #RAG experts at @LangChainAI @vercel @pinecone @anyscalecompute and get hands-on with intensive guided trainings? The 2-day RAG Developer Bootcamp is for you! Learn more & register now 👉https://t.co/Hf2b7x4LEo #llm #ml #rag #ai #vectordatabase #ray #pinecone https://t.co/5Bzh8G6eg1
Retrieval is one of the most important component of your RAG application. 💪 After evaluating multiple tools here is how I built a Studio for search and retrieval from PDF documents. 🔽 A minimal reproducible pipeline to retrieve semantically similar documents based on the… https://t.co/q5TmINSUm0
What’s the easiest way to specialize an LLM over your own data? Recent research has studied this problem in depth, and RAG is way more effective (and easier to implement) compared to extended pretraining or finetuning… Knowledge from pretraining. A lot of factual information is… https://t.co/mti03tpult
Building a @Wikipedia Chatbot with @AstraDB, @LangChainAI, and @Vercel ⬇️ #RAGChatbot #VectorDB #DataStax via @crtr0 https://t.co/owfKHiHo3p
Let’s talk about how easy it is to build your next RAG app 👇 @llama_index @pinecone @GoogleAI #gemini A step-by-step tutorial for beginners! https://t.co/KlM02NMdy1
OpenAI is the most well-known #LLM—but it's not the *only* LLM. Get a step-by-step look at how to build a basic conversational #RAG app using Nebula, #Milvus, MPNet V2, and LangChain.👇 https://t.co/pJOjNhWS1Z
Today we’re joined by @halfabrane, VP of engineering at @pinecone. In our conversation, we discuss vector databases and RAG along with its advantages & complexities with LLMs and vector DBs, deploying real-world RAG apps, & Pinecone Serverless (1/7) 🎧/📷 https://t.co/2YjpuqhcyI https://t.co/Df1ixqXNda
We are launching a research project that provides hands-on fine-tuning services to potential customers with complex use cases, particularly those focused on enhancing RAG. Our goal is to understand the optimal timing for companies to consider fine-tuning open source models,…
How do you evaluate LLMs, particularly for question answering and RAG? This research blog dives into approaches we've considered: https://t.co/vQXpVtq53t #ElasticSearchLabs #RAG #GenerativeAI
🌾 RAG vs Fine-tuning: pipelines, tradeoffs, and a case study on Agriculture. In this paper by Microsoft, they compare the two most common ways of incorporating domain-specific data when building applications with LLMs: (RAG) and Fine-tuning https://t.co/xDHEkaHUNe #llms https://t.co/GR8NiD3GrB
Redefining Retrieval in RAG A nice comprehensive study that focuses on the components needed to improve the retrieval component of a RAG system. Confirms that the position of relevant information should be placed near the query. The model will struggle to attend to the… https://t.co/zLouMdmXBy
RAG-Enabled LLMs On Web Search LLMs are extremely good at information retrieval and organization at scale. Systems that combine web search and LLMs make for extremely powerful tools that can help inform and educate us on any topic. It's like you have all of humanity's… https://t.co/LFLADpUr5k
What are the key ingredients to Retrieval Augmented Generation (RAG)? Read a great oevrview by @Abridgwater in @TechZineEU. #DataStax #GenAI https://t.co/laPjQUTZoa
RAG or Fine-tuning 🤔 What's better? RAG? Fine-tuning? or a combination? @Microsoft created a detailed case study on RAG and fine-tuning for domain-specific applications here in the agricultural sector. 👩🌾 A must-read for everyone who wants to learn or refresh his knowledge on… https://t.co/1xuST082nP
LlamaIndex just introduced RAG CLI 🔥 Simplest way to build RAG applications from the comfort of command line. Effortlessly index any file with a single line of code and uncover answers to your questions through a simple search command. To build a RAG application, follow…
LlamaIndex just introduced RAG CLI 🔥 Simplest way to build RAG applications from the comfort of command line. Effortlessly index any file with a single line of code and uncover answers to your questions through a simple search command. To build a RAG application, follow these…
We’re excited to partner with @ExaAILabs - they’ve created the most advanced RAG-powered web search we’ve seen so far. 🌐🔥 Unlike web search for humans, @ExaAILabs is tailor-made for LLMs, returning the most relevant highlights through custom chunking / extraction / retrieval.… https://t.co/HXLMBMJZKc https://t.co/Ptqe8zHEek
Check out RAG CLI! One of the easiest ways to get started on your RAG journey, made by none other than @llama_index's @thesourabhd https://t.co/ggcRdEjd3w
You can now easily build ✨scalable✨ #RAG apps using the #Zilliz Cloud Pipeline and @Llama_Index. Learn how with this tutorial 👇 https://t.co/yTBIDYCMOF
Looking forward to chatting with our partners at @PortkeyAI at @lightspeedvp SF about the trends we're seeing in productionizing RAG platforms. As always, there will be a few bits of alpha you won't find anywhere else. Sign up here: https://t.co/W4LPJNGMnF
Using RAG to answer your questions! Try out https://t.co/b7NTh8QQsk — an AI-powered chatbot based on the most popular Wikipedia pages. Tech stack ◆ @LangChainAI 's JS SDK ◆ @DataStax Astra vector DB ◆ @cohere ◆ @OpenAI ◆ @vercel's AI SDK https://t.co/j0X59uZDkn
Introducing RAG CLI 🧑💻🔎 - a dead-simple command-line tool that allows you to RAG literally any file on your local machine. Index any files including glob patterns, such as `$ llamaindex-cli rag --files "./docs/**/*.rst”` To search simply do `$ llamaindex-cli rag --question… https://t.co/QYW21JTwFi
Build a web RAG chatbot with @exaailabs (congrats on the launch!) We hosted it with LangServe, play around with it here: https://t.co/bdsn2HsCao https://t.co/gohoysfeCj
🚀Building a web RAG chatbot Our newest YouTube video on building with LangChain features how to build and end-to-end app (notebook to hosting) for a web RAG chatbot Video: https://t.co/iOVOgiHbYl To celebrate the update of @exaailabs (prev. Metaphor), we've teamed up to show… https://t.co/L1LMwqGXCP
You can now easily build ✨scalable✨ #RAG apps using #Zilliz Cloud Pipeline and @LlamaIndex. Learn how with this tutorial 👇 https://t.co/IBo3eXbsKq
In this blog, we are enhancing our Language Model (LLM) experience by adopting the Retrieval-Augmented Generation (RAG) approach! Don't miss out! Read more in this blog: https://t.co/JNwxvJzgS6 https://t.co/Smao6CMe4F
Ever wish you could live chat with @Wikipedia? Then say hello to Wikichat — a RAG chatbot built using Astra DB, @LangChainAI, and @Vercel. 🤖💬 https://t.co/AprTmkgkG8 #RetrievalAugmentedGeneration #VectorDB #DataStax https://t.co/dQ1Ldf9smd