Retrieval Augmented Generation (RAG) is a critical advancement for enhancing Large Language Models (LLMs) in enterprise settings. RAG reduces hallucinations, provides relevant and recent answers, and is cost-efficient. Companies like Teknium1 and NousResearch are implementing RAG to improve LLM capabilities by accessing external data and knowledge graphs.
Elevating the performance, accuracy, and verifiability of #LLMs with the next-gen of Retrieval Augmented Generation (#RAG), the decentralized Retrieval Augmented Generation (#dRAG) on @origin_trail - check it out 👇 https://t.co/yl61wyuz6P
Retrieval-augmented generation (RAG) is the best way to specialize an LLM over your own data. Researchers have recently discovered a finetuning approach that makes LLMs much better at RAG... RAFT and specializing LLMs. Most use cases with LLMs require specializing the model to… https://t.co/1Da0RPuxJc
🆕📨 Newsletter 🚀 We're covering the Efficient Frontier of LLMs—achieving better, faster, cheaper AI solutions and how Knowledge Graphs are are being integrated into RAG systems for more coherent and accurate outputs 🌐 #LLM #GenAI #RAG https://t.co/EVf9AjEk8l
What is RAG? RAG improves the accuracy and depth of LLMs by enabling them to access external data 🎯 https://t.co/d7TfKwcrG6
Autonomous RAG with @Teknium1 Hermes 2 Pro running on @ollama. Giving the LLM functions to search its knowledge base, greatly improving RAG capabilities. Spectacular work by @NousResearch🔥 code: https://t.co/PInQqMyv36 https://t.co/pxK8Skgdms
Why is Retrieval Augmented Generation (RAG) critical for using LLMs in an enterprise environment? 🤖 Reduces hallucinations 💡 Offers relevant, recent, and permissions-aware answers 💰 Cost-efficient and effective https://t.co/4MedRCQ5lh