freeCodeCamp·2yLearn RAG Fundamentals and Advanced Techniques
Learn about Retrieval-Augmented Generation (RAG) through a comprehensive course by Paulo Dichone on the freeCodeCamp.org YouTube channel. The course covers fundamental concepts, system building, advanced techniques like query expansion, and hands-on projects. By the end, you'll be equipped with the knowledge and skills to build and enhance RAG systems.
Towards AI·2yThe Best Practices of RAG
Explores the process of retrieval-augmented generation (RAG) and outlines best practices for its various components. Discusses query classification, efficient document retrieval, re-ranking for relevance, re-packing into structured formats, and summarization to extract key information. The post also provides a comprehensive evaluation of these practices and concludes with insights and recommendations.
Machine Learning News·2yTuring-Complete-RAG (TC-RAG): A Breakthrough Framework Enhancing Accuracy and Reliability in Medical LLMs Through Dynamic State Management and Adaptive Retrieval
Medical large language models (LLMs) face challenges such as inaccuracies and outdated information. The innovative TC-RAG framework introduces dynamic state management and adaptive retrieval, significantly improving model accuracy and reliability. By leveraging a memory stack system, TC-RAG minimizes errors and adapts to evolving medical knowledge, proving to be a powerful tool in medical analysis and decision-making.
CircleCI·2yBuild and test a LlamaIndex RAG application
LlamaIndex is a framework for building context-augmented large language model (LLM) applications. This tutorial walks you through creating a question-answering system using LlamaIndex, Python, and Flask, and exposing it as a REST API. It includes steps for setting up the environment, installing dependencies, defining scripts, writing unit tests, and automating the build and test process using CircleCI.