Testing Generative AI Systems

Overview

This course equips professionals with the skills to test, evaluate, and govern generative AI systems in real-world conditions. Participants will learn how to move beyond traditional deterministic testing approaches and adopt new methods suited to probabilistic, evolving AI behaviour.

Using practical examples, structured testing frameworks, red-teaming exercises, and hands-on evaluation activities, learners will explore why generative AI systems fail, how risks emerge over time, and how to design testing strategies that build confidence, safety, and trust, before and after deployment.

By the end of the day, participants will have:

• A practical testing strategy for a real generative AI use case
• A repeatable framework for evaluating quality, risk, and behaviour in GenAI systems
• Hands-on experience with red teaming and non-deterministic testing techniques
• Clear guidance on how to integrate GenAI testing into product and delivery lifecycles

Prerequisites

Participants should have:

• Basic familiarity with digital products, AI, or software delivery
• Exposure to AI-enabled features or systems (as a user, builder, or stakeholder)
• No prior experience in AI testing, data science, or machine learning required

Target audience

This course is designed for:

• QA engineers and test leads responsible for AI-enabled systems
• Product managers and product owners deploying generative AI features
• Developers and AI practitioners building or integrating GenAI models
• Risk, compliance, and governance professionals overseeing AI usage
• UX, CX, and innovation teams concerned with trust, safety, and reliability

Objectives

By completing this course, participants will be able to:

• Explain why traditional testing approaches fail for generative AI systems
• Identify key risk categories: hallucinations, bias, toxicity, privacy, and drift
• Design testing strategies for non-deterministic and evolving outputs
• Define meaningful benchmarks and evaluation criteria for GenAI quality
• Apply red-teaming techniques to surface hidden and adversarial failures
• Balance automation and human judgement in AI testing
• Embed GenAI testing into continuous delivery and governance practices

Outline

• Module 1: Why Generative AI Breaks Traditional Testing
• Module 2: Understanding GenAI Risk and Behaviour
• Module 3: Designing Tests for Non-Deterministic Systems
• Module 4: Benchmarking and Evaluation Criteria
• Module 5: Red Teaming and Adversarial Testing
• Module 6: Testing in Production and Governance

Exams and assessments

There are no formal exams in this course. Participants complete guided testing exercises, group challenges, and a final hands-on GenAI evaluation activity with peer and facilitator feedback.

Hands-on learning

• Analysis of real-world generative AI failures
• Group exercises designing GenAI test strategies
• Prompt-based variability and edge-case testing
• Red-teaming and adversarial testing sessions
• Iterative refinement of evaluation criteria and benchmarks
• Group reflection on risk, confidence, and system behaviour