?? New Course: Prompting for Testers
New Prompting for Testers course by Rahul Parwal

?? New Course: Prompting for Testers

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.


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Full Prompting for Testers course outline: What are prompts?

  • Describe what prompts are
  • Identify the features of prompts
  • Optimize an LLM response by adjusting different prompt parameters
  • List the various possibilities of leveraging LLMs using prompt engineering

Prompt engineering use cases in testing

  • Understand the use cases for prompting
  • Examine use cases for prompting in day-to-day testing tasks
  • Categorize testing tasks based on prompting categories

Essential prompting techniques

  • Understand the various prompting techniques
  • Design testing prompts using popular prompting techniques such as Few shots prompting, chain of thoughts, etc.

Context-driven prompting

  • Initiate the right context for any given testing task.
  • Classify & map various testing tasks to different temperature levels (prompt tuning)
  • Identify use cases and tasks where prompting and large language models (LLMs) might be unsuitable

Prompts for test data

  • Recognize a limitation of AI-generated test data and how to overcome it
  • Craft prompts to generate test data sets
  • Create test data generator utilities using prompting
  • Generate functions (code blocks) for dynamic test data generation based on custom needs for automation using prompts

Prompting for test ideas

  • Recognize good testing ideas translates to good testing
  • Generate awesome test ideas by blending mnemonics with prompt engineering
  • Evaluate the value of using mnemonics to generate test ideas

Prompting checklist: Secrets of good prompts

  • List the key ingredients of a good prompt
  • Identify and assess the quality of the ingredients in a given prompt
  • Develop testing prompts that adhere to proven good practices

Prompting hubs for testing

  • Access various sources of ready-to-use prompts
  • Create your prompting hub with custom prompts
  • Utilize tools to maintain prompts for testing purposes

Next steps: Develop your prompt engineering plan

  • Learn what to do after completing the course on prompt engineering
  • Reinforce?learnings and take the next steps on your journey
  • Stay updated with the latest advancements in prompt engineering


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Neenu Ann Shaji

Experienced Test Engineer | 5 Years in Manual Testing | Passionate About Quality Assurance

2 个月

Is there any fees for this course

Kunal Punjabi (KP)

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.

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Godwin Josh

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?

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