Testing and Refining LLM Applications

This course is designed for software engineers and ML practitioners aiming to advance from building LLM prototypes to deploying robust, production-grade AI systems. In the real world, a reliable application requires more than a clever prompt; it demands a rigorous software engineering foundation to ensure its testability, maintainability, and safety. This course provides that critical toolkit. You will learn to apply Test-Driven Development (TDD) to methodically build and refactor LLM-powered microservices, ensuring that your code is clean and verifiable from day one. To safeguard your applications, you will create sophisticated behavioral test suites that enforce safety policies and prevent undesirable outputs. You'll go a step further by using mutation testing to evaluate the quality of your own tests, ensuring that your safety guardrails are truly effective. The course also dives into the MLOps lifecycle, teaching you to version datasets and models with DVC, track experiment results on platforms like W&B, and make data-driven decisions about the models to promote. Finally, you will learn to automate your entire testing and evaluation workflow using powerful Python scripts, thereby preparing your application for seamless integration into a CI/CD pipeline

• Apply TDD to microservice endpoints and refactor modules to improve code quality
• Develop behavior and safety tests to ensure compliant LLM outputs
• Use data versioning to track artifacts and evaluate ML experiment runs
• Create Python scripts to automate workflows in cloud environments

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work