Episode 8: Prompt and Circumstance - Prompt Engineering for Testers

In this episode, we delve into the world of Prompt Engineering, specifically tailored for software testers.

Discover how to extract the most precise information from Large Language Models (LLMs) using three distinctive frameworks:

 • RACE: Role, Action, Context, Expectation

• COAST: Context, Objective, Actions, Scenario, Task

• APE: Action, Purpose, Expectation

Learn why it’s crucial to define the AI’s role and provide ample context to ensure accurate responses. Find out how to refine your queries if you’re unsatisfied with the answers, and explore techniques like asking for more details or inquiring about the AI’s confidence in its response.

 

We also explore innovative ways to enhance your testing processes, such as uploading UI screenshots to generate test cases or even having the AI produce test cases in CSV format for seamless integration with your test case management system.

 

Additionally, we discuss the intriguing decline in Stack Overflow traffic due to the rise of AI, and the potential long-term impact on AI quality as these models rely on platforms like Stack Overflow for training data.

 

Tune in to gain valuable insights and elevate your software testing with the power of AI!

 

More Resources:

• https://www.linkedin.com/pulse/prompting-testers-jason-arbon/
• https://testsigma.com/blog/prompt-engineering-for-testers/
• https://www.linkedin.com/feed/update/urn:li:activity:7256493948087476224/