Josep Curto’s Post

RAG (Retrieval-Augmented Generation) is a framework designed to improve the performance of #GenAI #models by incorporating external information retrieval mechanisms. RAG combines the strengths of retrieval-based and generative approaches to deliver superior performance in information-intensive tasks. #RetrievalAugmentedGeneration #niceread ZDNEThttps://lnkd.in/gAADtAGj

What's the fastest, most advanced, innovative and badass way to Fine-Tune and Deploy the latest open source AI models? Watch this demo of Monster API's Fine-tuning Agent. and try the GPT here: https://lnkd.in/gYhWTC-r

🤝 From Day 0, We Support Llama 3.1 405B! 🤝 ScrapegraphAI supports Llama 3.1 405B, the largest and most capable open-source model, thanks to our seamless integration with Groq, Bedrock, and Hugging Face connectors. 😱 This powerful model surpasses GPT-4o 😱 , ensuring enhanced quality and performance in our scraping tasks. Let's take our data collection to new heights with this incredible open-source model! Join us in leveraging this cutting-edge technology to make the most out of your web scraping endeavors. Go to the first comment for the repo link 👇 #AI #WebScraping #MachineLearning #DataScience #ScrapegraphAI #Llama3.1 #OpenSource #TechInnovation

For anybody trying to figure out how to feed AI models data and get good results you might want to look at this. Basically they go through a lot about RAG and things like splitting and semantics to help you understand how to work with your data and AI . So hard to find good material on this topic but this was quite useful. #RAG #AI #datastax https://lnkd.in/eaussQW6

RAG answer quality can be greatly improved by helping it discover entities and relations via graph, which traditional semantic embedding-based retrieval can't do. This helps us answer convoluted questions like crossover events and entity interactions. We can feed in structured data (via Graph) with entities and relationships and unstructured document chunks to LLM and instruct it to provide a better answer. https://lnkd.in/gwNAVyG8 #GraphIntelligence #RAG #AI #LLM

🚀 𝑼𝒏𝒍𝒐𝒄𝒌 𝒕𝒉𝒆 𝑭𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝑨𝑰 𝒘𝒊𝒕𝒉 𝑹𝒆𝒕𝒓𝒊𝒆𝒗𝒂𝒍-𝑨𝒖𝒈𝒎𝒆𝒏𝒕𝒆𝒅 𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏 (𝑹𝑨𝑮)❗ 🔍 . . . Are you ready to dive into the next big thing in AI? If you're looking to supercharge your machine learning models with powerful retrieval capabilities, RAG is your go-to solution. I’ve just published a deep dive article on Medium about 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗥𝗔𝗚: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀, where I break down the mechanisms behind RAG and how it’s transforming the AI landscape. In this article, I explore: 🔹 How RAG works behind the scenes 💡 🔹 Advanced techniques for optimizing RAG pipelines 🔧 🔹 Real-world applications of RAG in AI-driven industries 🌍 Whether you're a data scientist, ML engineer, or AI enthusiast, mastering RAG can elevate your skills to the next level. #RAG #AI #MachineLearning #GenerativeAI #ArtificialIntelligence #DeepLearning #AIResearch Zia Khan Muhammad Qasim Ameen Alam Hamzah Syed Muhammad Junaid Shaukat Okasha Aijaz Harrison Chase Brandon Hancock Andrew Ng LangChain OpenAI

GraphRAG 101: Increasing GenAI Accuracy and Completeness... GraphRAG supplies a “knowledge graph” to an LLM. Unlike text documents, these data structures make the relationships between things explicitly clear.

Unlock the power of HybridRAG, the innovative AI system that enhances language models with superior accuracy and contextual recall through knowledge graphs and vector retrieval methods. #AI #LLM #Agent #RAG #HybridRAG #GraphRAG 

Sebastian Raschka, PhD and Manning Publications Co. kindly sent me the book "Build a Large Language Model from Scratch". Almost everyone in the world uses LLMs (ChatGPT, Gemini, Llama...) without knowing how they work. These people are enchanted by the “magic” of AI. For anyone who wants to discover the trick behind these language models, this book is a must-read. This is the first (and best) detailed explanation of the entire process (data preparation, attention mechanism, architecture, pretraining, finetuning...) for creating an LLM that I have ever seen. http://mng.bz/46ov

DeepSeek from China unveils the R1-Lite-Preview, a "reasoning" AI model that rivals and surpasses OpenAI's o1-preview in various benchmarks. https://lnkd.in/dGYfBJB5 #ai #DeepSeek