?? New Course: Prompting for Testers
Ministry of Testing
Ministry of Testing is where software testing professionals grow their careers.
As Generative AI continues to gain popularity, knowing how to use it effectively can make a big difference in your daily testing tasks and long-term career goals. A key skill in making the most of this technology is?prompt engineering—the practice of designing and refining prompts to get the best results from large language models (LLMs). This course is for testers who want to add this essential, practical skill to their testing toolkit.
Whether you're new to testing or have years of experience, this course will introduce you to the practical side of prompt engineering, combining video lessons, hands-on exercises, interactive activities, and even a few fun games to reinforce your learning.
By the end of this course, you’ll be able to confidently create and refine powerful prompts, making Generative AI a valuable tool in your everyday work. Unlock this course as part of Ministry of Testing's Professional Membership.
Full Prompting for Testers course outline: What are prompts?
Prompt engineering use cases in testing
Essential prompting techniques
领英推荐
Context-driven prompting
Prompts for test data
Prompting for test ideas
Prompting checklist: Secrets of good prompts
Prompting hubs for testing
Next steps: Develop your prompt engineering plan
Experienced Test Engineer | 5 Years in Manual Testing | Passionate About Quality Assurance
2 个月Is there any fees for this course
Vice President @ Kotak Mahindra Bank ?? Seasoned IT Professional ???? 15k+ Connections ?? Continuous Learning ??
2 个月Indeed well articulated , Prompt Engineering is the future with Gen AI rapidly picking up pace.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
2 个月Prompting is evolving beyond simple input; it's about crafting nuanced queries that guide AI models towards desired outputs. Techniques like few-shot learning and prompt engineering are crucial for achieving this, allowing testers to specify expected behaviors and edge cases. The integration of reinforcement learning in prompting paradigms presents exciting possibilities for adaptive and self-improving test strategies. How can we leverage the concept of "prompt distillation" to create more concise and efficient prompts while maintaining accuracy in automated testing scenarios?