Accelerate your testing processes with Generative AI

A practical training to integrate AI for testing into your daily routine. More than 70% hands-on practice

Training Description

Learn how to effectively integrate generative AI into your software testing processes with our practical training program “Accelerating Testing Processes with Generative AI”.

Throughout this training, you will explore the fundamentals of AI for testing, adopt best practices, and discover tools to optimize your testing processes.

You will develop key skills to create relevant prompts and manage error risks associated with AI models. Learn to master conversational AI to query various LLMs effectively and integrate them into your tool chains.

Through practical exercises, you will have the opportunity to experiment with a variety of AI models and tools for testing. Optimize your testing processes and enhance the quality of your applications by leveraging generative AI.

By the end of the training, you will be able to identify the opportunities, limitations, and risks associated with the use of generative AI for testing.

Objectives and Learnings

🧠 Gain a comprehensive understanding of generative AI for testing.

🚀 Learn effective querying techniques.

🔧 Discover and experiment with AI tools for testing.

🤖 Identify the limitations and risks of AI-augmented testing.

Training Program

1- Introduction to Generative AI for software testing

  1. Generative AI – The basics
  2. What generative AI brings to software testing
  3. Using generative AI for testing – General principles
    • Practical exercise 0: Getting familiar with the LLM workbench

2- Querying a large language model for testing – how to get good results

  1. Introduction to prompt engineering
  2. Prompting techniques 
  3. Use cases with practical exercises:
    • Practical exercise 1 – Designing test cases
    • Practical exercise 2 – Improving existing test cases
    • Practical exercise 3 – Test automation
    • Practical exercise 4 – Analyzing anomaly reports
  4. Good prompting practices in software testing
  5. Summary and discussion of the know-how used

3- Managing the risks of Generative AI

  1. Hallucinations, Errors, and Bias of AI
    • Practical exercise 5 – Hallucinations / Errors – Examples from the testing domain
  2. Cybersecurity risks
  3. Environmental risks
    • Practical Exercise 6 – How much does it cost in energy?
  4. Other risks: Loss of skills, dependence on a third-party service
  5. Regulation of AI: The European AI Act

4- LLM-based applications for testing

  1. Applications for testing including generative AI
  2. Retrieval Augmented Generation (RAG) – Using company/project data
    • Practical exercise 7 – Questioning a large document corpus
  3. Integrating generative AI into the testing process
    • Fine-Tuning of the LLM
    • Practical exercise 8 – Integration of generative AI functions in a test tool
    • LLMOps
  4. AI agents for testing
    • Demo: Gérald – Virtual Manual Tester
  5. Summary and discussion 

5- Summary: What we’ve learned 

  1. How to use Generative AI in practice
  2. Choosing the most appropriate AI model for testing
  3. Conclusion

Training in German

Register via our partners

Training in English

Contact Anne Kramer directly at: or

Our trainers

Bruno Legeard

Bruno is an expert in AI for testing. He is involved in the development of AI-integrated tools at Smartesting and in evaluating generative AI for their application in software testing. He also actively contributes to the French Software Testing Committee and to ISTQB within technical working groups.

Anne Kramer

Since her first model-based testing project in 2006, Anne has continuously promoted MBT – as project manager, consultant, trainer, and author. In 2022, she joined Smartesting, where she discovered a whole new type of models: Large Language Models (LLMs) and Generative AI. With the support of Smartesting’s AI experts, Anne gained in-depth knowledge of using ChatGPT. It is this knowledge that she now wishes to share in this training.