Enhancing AI-Driven Quality Assurance
summary
Claims that prompt engineering measurably increases accuracy, speed, and reliability of AI-driven QA by turning test-case specs into repeatable prompts, iterating them against test suites, and tracking versions with Prompt Layer. Demonstrations show prompts generating hundreds of formatted test cases plus downloadable Excel outputs, a working custom GPT documentation chatbot that returns up-to-date code, and a structured Anthropic workflow for test → refine → share. Mentions agents, knowledge memory, and prompt tuning as production-ready levers.
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chapters (14)
- 0:00Introduction and Speaker Background
- 0:31Overview of NearForm and Session Goals
- 1:15Understanding Prompt Engineering
- 2:07Prompt Engineering in Quality Assurance
- 5:27Steps for Effective Prompt Engineering
- 8:32Examples of Good Prompts
- 9:40Challenges in Traditional Test Automation
- 10:24Advanced Prompt Engineering Techniques
- 11:35Managing and Tracking Prompts
- 12:59Custom GPTs and Documentation Chatbots
- 16:04Using Agents and Knowledge Memory
- 18:51Prompt Engineering in QA Phases
- 23:49Conclusion and Key Takeaways
- 26:44Final Thoughts and Contact Information
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