Evaluating RAG using Llama 3

Don't fall behind the LLM revolution, I can help integrate machine learning/AI into your company. AI Freelancing: https://mosleh587084.typeform.com/to/HSBXCGvX Code: https://github.com/mosh98/RAG_With_Models/blob/main/evaluation/RAGAS%20DEMO.ipynb This comprehensive video provides an in-depth exploration of the Ragas framework, a pivotal tool designed to assess and enhance your Retrieval Augmented Generation (RAG) pipelines. RAG is about using Large Language Models (LLMs) that utilize external data to expand the context of the model, leading to more informed and accurate outputs. In this tutorial, we delve into the challenges of evaluating RAG pipelines, and how the Ragas framework (RAG Assessment) revolutionizes this process. You'll learn about the unique features of Ragas, its integration with existing tools, and practical tips on how to effectively quantify and improve the performance of your RAG applications using the latest advancements in AI, specifically Llama 3. RAGAS github: https://github.com/explodinggradients/ragas RAGAS Docs: https://docs.ragas.io/en/stable/ Why use Llama3? Unlock the limitless potential of Meta Llama 3 for unparalleled AI capabilities. With state-of-the-art performance, enhanced reasoning, and a commitment to responsible innovation, Llama 3 redefines what's possible in the world of open source language models. Experience the future using Meta Llama 3. Brief info on RAGAS 0:00 Install Llama 3 locally 0:54 Using RAGAS dataset 1:19 Integrating Llama 3 & RAGAS 2:34 Evaluating using RAGAS: 3:08