How To Test AI Applications

This is a free, hands-on course on testing AI applications.

Across three modules you’ll master the unique nature of AI systems and learn to calculate the metrics AI teams actually use: Precision, Recall and F1-Score, Mean Reciprocal Rank, semantic-search and ranking indicators, and the LLM metrics behind generation quality.

You’ll practice prompt engineering, learn to defend models against prompt injections and jailbreaks, and explore non-functional AI testing—with theory, quizzes and hands-on exercises in every module.

This course is NOT about using AI tools to write test cases or autotests for traditional applications—any active QA engineer can learn that on their own.

This course IS about testing AI applications themselves—one of the first hands-on courses in the field, with zero setup required.

No Python or advanced math required. We focus on the core essentials while mapping your future career path with high-level overviews of advanced topics.

Covered in depth:

Non-LLM metrics, LLM metrics, Prompt engineering, Injections & Jailbreaks, Non-functional AI testing, LLM-as-a-Judge

Overview level—for your career map:

AI agents, RAG systems, Fine-Tuning, MLOps, Data testing, Gray-box AI testing, Choosing the right AI model

Gain hands-on skills testing five leading AI models directly in your browser with the Mentorpiece Sim simulator. Frontier AI model APIs are paid, but we credit every student with 5,000 tokens at our own expense when they register in the simulator.