Understanding Vibe Coding and AI Tools
- Vibe coding: AI-assisted code generation using natural language.
- AI tools like Cursor simplify coding without deep technical setup.
- Current limitations exist in AI tool integration and maturity.
- AI enables faster coding, test fixing, and bug prediction.

Changes in QA Role and Responsibilities
- Traditional QA as “bug hunter” is evolving.
- QA becomes more of a “sensei” or facilitator ensuring quality comprehensively.
- AI helps test idea generation (agentic testing), predicting user behavior and test outcomes.
- Everyone on the team now contributes to quality, raising challenges about ownership responsibility.

Key Areas of AI Impact in QA
- Self-healing tests: automatic fixing of broken tests.
- Test design augmentation: AI supports generating and refining test cases.
- Bug prediction: AI analyzes past bugs and predicts potential issues.
- Data thinking: understanding user behavior and smart test data selection.
- Prompt engineering: crafting precise AI prompts is critical for success.

Challenges and Considerations
- False positives: AI-generated or automated tests may pass but miss real issues.
- Human verification remains essential to ensure test validity, context relevance, and user-centered evaluation.
- AI cannot replace human empathy or critical thinking.
- Responsibility ambiguity: when all test, who truly owns quality?

Actionable Items / Tasks
- Learn to generate simple test cases with AI tools.
- Practice bug prediction using AI prompts.
- Use AI like ChatGPT for generating test reports.
- Experiment with different prompt techniques (one-shot, few-shot).
- Leverage AI for test data generation and Postman collections.
- Train to write clear, domain-specific AI prompts.
- Maintain a critical QA mindset: question if system behaviors make sense for users.
- Be prepared to manually verify AI results and maintain responsibility for quality assurance.

Future Outlook and Discussion
- QA role is shifting from purely technical testers to strategic quality facilitators combining AI use with human insight.
- AI accelerates tasks but demands new skills, particularly in prompt engineering and critical product understanding.
- The quality profession will become more complex, requiring adaptation and continuous learning.

Conclusion
- Embrace AI as a tool to enhance QA effectiveness but rely on human oversight.
- The QA role evolves beyond bug hunting to quality leadership incorporating AI capabilities.
- Continual dialogue and experimentation with AI in QA practices are recommended.