ISTQB Generative AI Training

Day 1: Generative AI Fundamentals and ISTQB Concepts

• Introduction to generative AI systems and language models

• Model architectures: LLMs, GANs, Diffusion Models, Transformers

• Training data, dataset bias, and impact on testing

• Challenges of testing AI-based systems

• Differences between traditional testing and AI testing

• Functional and non-functional testing applied to AI systems

• Introduction to ISTQB testing techniques for AI

Day 2: Advanced Testing Strategies and Validation

• Designing test strategies for generative AI systems

• Testing for robustness, performance, and reliability

• Bias detection and mitigation techniques

• Output validation: accuracy, coherence, relevance, and consistency

• Introduction to AI testing automation tools

• Monitoring and auditing AI models in production

• Test documentation and reporting aligned with ISTQB standards

Day 3: Practical Scenarios and Knowledge Integration

• Hands-on exercises: designing AI test scenarios

• Testing LLMs, image generation models, and recommendation systems

• Implementing AI testing pipelines and automation workflows

• Advanced analysis of AI quality metrics and KPIs

• Ensuring security, compliance, and ethical AI validation

• Real-world case studies and best practices

• Integration of ISTQB guidelines into AI QA processes

The ISTQB Generative AI Training provides a structured and advanced approach to testing AI systems, a rapidly growing and critical field in modern software development.

By attending this training, you will be able to:

• Develop expertise in testing generative AI systems and models

• Apply ISTQB standards to AI-driven environments

• Design and execute functional and non-functional AI tests

• Identify and mitigate biases and model inconsistencies

• Build automated and scalable AI testing strategies

• Ensure reliability, performance, and ethical compliance of AI systems

• Gain highly demanded skills in QA, AI, and data-driven environments