Why take this course
Would you like to harness the power of Generative Artificial Intelligence in software testing and become a specialist in this rapidly growing field?
This training provides advanced knowledge and practical skills required to effectively use Generative AI (GenAI) and Large Language Models (LLMs) throughout the software testing lifecycle. You will learn prompt engineering techniques, AI risk management, and how to integrate LLM-based infrastructure into testing processes.
The course also prepares you for the certification exam leading to the internationally recognized CT-GenAI credential.
What you'll learn
- Understand the fundamental concepts, capabilities, and limitations of Generative AI in software testing.
- Apply prompt engineering techniques for test analysis, test design, automation, and reporting.
- Generate test cases, acceptance criteria, and synthetic test data using AI.
- Identify and mitigate AI-related risks, including hallucinations, reasoning errors, bias, and data privacy violations.
- Understand the architecture of LLM-based testing infrastructure, including RAG, LLMOps, and AI agents.
- Integrate GenAI solutions into testing environments.
- Contribute to the development of a GenAI adoption strategy and roadmap within a testing organization.
Who is this course for
This training is designed for professionals involved in applying Generative AI to software testing, including:
- Software Testers
- Test Analysts
- Test Automation Engineers
- Test Managers
- User Acceptance Testers (UAT)
- Software Developers
The course is also suitable for:
- Project Managers
- Quality Managers
- Software Development Managers
- Business Analysts
- IT Directors
- Management Consultants
Topics covered
Day 1: Foundations, Prompt Engineering, and Risks
Focus: understanding the technology, creating effective prompts, and managing risks.
- Block 1: Introduction to GenAI in Software Testing
- Fundamentals of Generative AI
- Key concepts:
- Large Language Models (LLMs)
- Tokens
- Context
- Core principles of using GenAI in testing processes
- Block 2: Prompt Engineering for Effective Testing
- Creating effective prompts for testers
- Applying advanced prompt engineering techniques in practice
- Evaluating AI-generated outputs
- Iterative prompt improvement and optimization
- Block 3: Managing GenAI Risks in the Testing Process
- AI hallucinations
- Reasoning errors
- Model bias
- Data privacy concerns
- Cybersecurity risks associated with AI systems
Day 2: Technical Solutions, LLMOps, and Implementation
Focus: architecture, operationalization, and organizational adoption strategies.
- Block 4: Regulations and Standards
- AI regulations and compliance requirements (e.g., AI Act)
- Industry standards and best-practice frameworks
- Energy consumption and the environmental impact of Generative AI
- Block 5: LLM-Based Solutions for Testing
- Architectural approaches for building LLM-powered testing tools (e.g., Retrieval-Augmented Generation – RAG)
- Fine-tuning AI models
- LLMOps fundamentals
- Operationalizing AI solutions within testing environments
- Block 6: Implementing and Integrating GenAI in Testing Organizations
- Building a GenAI adoption roadmap for QA teams
- Organizational change management during the implementation of generative AI technologies
- Best practices for integrating GenAI into existing testing workflows
