RAG-LLM Evals & Test Automation for Beginners

Evaluating and testing AI-based systems built on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) requires a fundamental shift from traditional software testing paradigms. This comprehensive course, designed by the Rahul Shetty Academy, equips quality assurance engineers, automation testers, and AI developers with the modern frameworks necessary to rigorously assess and validate LLM systems. Students will dive deep into the core mechanics of RAG architectures and learn to address common issues like hallucinations, poor context retrieval, and inaccurate response generation. The curriculum guides learners through practical implementations using the RAGAS evaluation framework combined with Python and Pytest. You will master both single-turn and multi-turn conversational evaluation techniques while generating synthetic test data specifically designed to measure complex LLM metrics. By the end of this course, you will have built a production-ready, fully automated test suite capable of asserting and benchmarking LLM metrics systematically. This is an essential skill set for QA professionals looking to stay relevant in the rapidly growing field of AI automation and cognitive system testing.