The use of Retrieval-Augmented Generation (RAG) in the AI landscape is gaining popularity, with various resources available for implementation. Companies like LangChainAI and SpaceandTimeDB are collaborating to enhance RAG applications. RAG combines external knowledge with generative AI models like Large Language Models (LLMs) to improve output and responses. Evaluation frameworks and tools are being developed to assess RAG systems' retrieval accuracy and generation quality, emphasizing the importance of RAG in enhancing AI capabilities.
These open-source RAG tools just may be the difference maker between a good LLM and a great LLM. #DataScience #AI #ArtificialIntelligence https://t.co/t6jHZQYVWp
With Retrieval-Augmented Generation (or #RAG), a chatbot can respond like a human with deep institutional knowledge about the company, its products, and its policies. Learn why RAG matters, how it works, and its benefits here: https://t.co/mUtk8U2wM9 #KnowledgeGraphs https://t.co/kVtOgW2VYV
Retrieval Augmented Generation (RAGs), clearly explained:
A Gentle Introduction to Retrieval Augmented Generation (RAG) for the Intelligence Community! RAG has revolutionized how we access and utilize data, offering real-time information retrieval to enhance language models. Dive deep into this blog on the importance of RAG,…
Don't miss our webinar TODAY that will introduce you to the basics of #RAG, demonstrating how it enhances the capabilities of AI systems by integrating retrieval mechanisms into generative models 💻 Register: https://t.co/ondhS5XtOJ #AI #database #SingleStore
Do you know what RAG is and why it is a significant advancement in AI technology? Find our in our webinar TOMORROW! 🚨 Register now! https://t.co/ondhS5XtOJ #AI #database #SingleStore
[CL] Evaluation of Retrieval-Augmented Generation: A Survey https://t.co/6Ta1QjuZRo - This paper provides a comprehensive survey and analysis of evaluation methods for Retrieval-Augmented Generation (RAG) systems. - It summarizes the key challenges in evaluating RAG… https://t.co/2IEApRQLK7
Although your data is distributed throughout numerous databases, you can still use ALL of it for your RAG application 🍱 In this notebook, we build an end-to-end RAG pipeline that uses #Google's Big Query and @weaviate_io, using DSPy! Context Fusion with Agents will first… https://t.co/lMeEBlHmIK
We’re collaborating with @hyperbolic_labs to power their research around verifiable RAG 🚀 Let’s talk about how, why, and what that means. 👇 What is RAG? Retrieval augmented generation (RAG) is when an LLM grabs fresh data or extra context from an external vector search… https://t.co/tcrvf5Tbra https://t.co/eVc6hV16Qe
We’re teaming up with Space and Time to build trust in AI. LLM’s are great with words, but not always with facts. We're joining forces with @SpaceandTimeDB to remedy this by building a verifiable solution for Retrieval-augmented Generation (RAG). Dig in to learn more!👇 https://t.co/Uy5uvOPBsi
Building and training an #LLM can cost a company millions depending on its size and needs.💰 Here's why we think choosing an open-source LLM might be worth it for your business. 🌟 Read here👇 https://t.co/Pbrz49E4YV
What RAG applications are you going to build with @weaviate_io? https://t.co/cFRuYFpRa5
Building a RAG pipeline (Retrieval-Augmented Generation pipeline) is something nearly all AI startups do these days. What is this, and how do you build one? Today's deepdive and code-along covers this, and then some more. By @rossmcnairn: https://t.co/bvyXuVFjCB https://t.co/eoMTyWTRmh
With @LangChainAI + Astra DB, building a RAG application is so easy. DataStax's Andy Gujral shows you how with just 50 lines of code. 🙌 Get started building: https://t.co/PKbXmAOjQ5 #LangChain #RAGApplications #JavaScript https://t.co/9M8FrrPdJk
Evaluation of Retrieval-Augmented Generation: A Survey Presents an analysis framework to systematically evaluate RAG systems by considering retrieval accuracy, generation quality, and additional factors 📝https://t.co/jQQDuHJ81X 👨🏽💻https://t.co/K1NzRKm2Zy https://t.co/ONDE4TDkDX
[CL] A Survey on RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models https://t.co/RHMgk35CFO - Retrieval-Augmented Generation (RAG) can provide reliable and up-to-date external knowledge to generative AI models like Large Language Models (LLMs), enhancing… https://t.co/9fL6ukXVzW
Think an #LLM is a one-size-fits-all solution? Think again! 💡 Dive into the nuances of using Large Language Models at #CDS2024 and learn the optimal use cases and strategies for extracting maximum value from LLMs in business. 🚀 Secure your spot! https://t.co/PC5GWVVa5T https://t.co/NxEPz8yUvj
Improving LLM Output by Combining RAG and Fine-Tuning https://t.co/Q33BjX0zu2 @conviva #AIEngineering #DataEngineering #LLMs #LargeLanguageModels #Observability https://t.co/J4yg0dk6Dt
A great way to create competitive advantage with generative AI platforms is to enable the AI to dynamically incorporate relevant external information (aka your data) into its response process. One of the best ways to accomplish this is by creating a RAG (Retrieval-Augmented… https://t.co/TLKdCz0OIp
RAG is still by far one of the most popular topics in the AI landscape. A list of practical resources to get started with RAG: - Webinars by @llama_index, @deepset_ai, @superannotate, @LangChainAI and @ragas_io - Workshops by Amazon Bedrock, @azure @openai Links and more: https://t.co/ENULZn6mwe