Imagine a world where AI not only understands your testing needs but also crafts precise test cases tailored to your unique context. In today’s rapidly evolving tech landscape, testers often struggle with Large Language Models (LLMs) that generate outdated or irrelevant outputs, lack access to proprietary data, and sometimes produce hallucinations—confidently incorrect information. These challenges arise from LLMs’ static knowledge bases and their inability to tap into specific organizational data. Enter Retrieval-Augmented Generation (RAG), a transformative approach designed to bridge these gaps. RAG enhances LLMs by integrating them with real-time data retrieval systems, ensuring responses are both current and contextually relevant. For testers, this means LLMs can now access and utilize up-to-date information from internal databases, leading to the generation of accurate and context-specific test cases. In this talk, we will take a deep dive into the architecture of RAG, explore its various components, learn how to set up a local RAG system and be introduced to RAGAS (Retrieval-Augmented Generation Assessment). We will also explore how to evaluate RAG systems based on key metrics such as context relevancy, faithfulness, and answer accuracy. Join me to discover how transitioning “From RAG to Riches” can revolutionize your testing processes, empowering you to harness AI’s full potential as your organization’s second brain.