The module "Retrieval Augmented Generation Evaluation and Observability" provides a comprehensive exploration of advanced techniques and tools in the field of AI, focusing on enhancing chatbots and question-answering systems through Retrieval-Augmented Generation (RAG) systems. It explores the critical aspects of evaluating these systems, emphasizing the importance of faithfulness, relevance, and the prevention of hallucinations in AI responses. The module introduces tools like the FaithfulnessEvaluator and RAGAS, along with the Golden Context Dataset, offering insights into effective evaluation methodologies, including indexing, embedding, and generation metrics. Additionally, the module covers the LangChain framework and the LangSmith platform, providing practical knowledge on building and testing LLM-powered applications. Students will learn about the components of LangChain, such as Models, Vector Stores, and Chains, and the functionalities of the LangChain Hub.
RAG - Metrics & Evaluation
In this lesson, you will learn about Retrieval-Augmented Generation (RAG) systems and their evaluation metrics, with a focus on improving chatbots and question-answering systems. The lesson introduces you to different approaches to analysis, the importance of faithfulness and answer relevancy, nuances of indexing and embedding metrics, and generation metrics aimed at preventing hallucinations in AI responses. It discusses the significance of the FaithfulnessEvaluator tool for checking the alignment of AI responses with retrieved context and introduces RAGAS and the Golden Context Dataset for system evaluation. Additionally, real-world setups for assessing and improving RAG systems are explored through examples of community-based tools, including comprehensive evaluation of retrieval metrics, holistic approach evaluations, and the custom RAG pipeline evaluation.
LangSmith and LangChain Fundamentals for LLM Applications
In this lesson, you will learn about the fundamentals of the LangChain framework and the newly introduced LangSmith platform to build and test LLM-powered applications. We will review LangChain components, such as Models, Vector Stores, and Chains, as well as principles of the LangChain Hub, including prompt exploration and versioning for collaborative prompt development. The lesson will guide you through setting up the LangSmith environment, creating an API key, and basics of prompt versioning and tracing. You will also learn how to use LangServe to deploy applications as a REST API and go through the process of reading and processing data from a webpage, storing it into Deep Lake vector store, and using prompts from the LangChain Hub to build a QuestionAnswering Chain application.