Effective AI Usage in Testing: From ChatGPT to Test Code Generation

Learning objective

AI is becoming a core part of modern software engineering workflows. This workshop equips testers with practical knowledge and hands-on skills to use tools such as ChatGPT and AI-powered IDEs (for example Cursor) in daily quality engineering work.

After completing the workshop, participants will be able to:

• understand how large language models (LLMs) like ChatGPT work,
• create effective prompts that improve testing productivity,
• use AI responsibly when generating and reviewing automated test code.

Scope

• LLM fundamentals and practical understanding of ChatGPT
• Key concepts: AI, NLP, LLMs, prompts
• Model mechanics: tokenization, embeddings, attention mechanism
• Transformer architecture and model training basics
• Prompt engineering methods and best practices
  • role prompting ("act as")
  • few-shot prompting
  • chain-of-thought style reasoning
  • challenge/critique prompts
  • language strategy for prompting
• Testing-focused AI applications
  • test data generation
  • test case generation
  • pair-testing with AI
  • using AI to accelerate learning and analysis
• Practical exercise: generating CI setup (GitHub Actions) with AI
• AI IDE workflows (Cursor and similar)
  • AI-assisted coding and refactoring for tests
  • semantic search and working with technical documentation
• End-to-end workshop labs
  • building automated tests with Playwright and JavaScript/TypeScript
  • evaluating what to delegate to AI vs what to keep manual
• Risk and quality controls
  • common AI failure modes and hallucinations
  • safe usage patterns in test engineering
• Next-step learning paths, including API-level AI usage

Preparation

Who should attend?

The workshop is intended for testers and QA engineers who know programming fundamentals (preferably JavaScript).

What to prepare

Participants should bring a laptop prepared according to trainer instructions provided before the workshop.

Teaching methods

The training is predominantly hands-on workshop work supported by short theory modules. Participants learn through practical exercises and guided implementation.

Training materials

• workshop presentation,
• ready-to-run code examples (organized in branches/modules),
• working notes and reference links,
• additional guidance materials used during labs.

Benefits

• Strong practical understanding of LLMs in a testing context.
• Ability to design prompts that improve output quality and usefulness.
• Better use of AI IDEs for test development and maintenance.
• Hands-on experience generating and validating automated tests with AI support.
• Clear understanding of AI risks and how to apply safe team practices.
• A practical workflow that can be reused in real QA projects.