AI on Pi Day Highlights

We are excited to announce a new column in our Forecaster article where we’ll be showcasing our sessions! You’ll find them listed in chronological order, and as a bonus, we’ll be providing some complimentary gifts. To find them, just check the presentation links. Easy peasy!

Prompt Engineering for Testers by Rahul Parwal

This article examines Prompt Engineering, a technique that enhances the capabilities of Large Language Models (LLMs) for testers. The author, Rahul Parwal, discusses how testers can leverage Prompt Engineering to enhance their testing skillset. The core concept of Prompt Engineering is optimizing prompts to elicit the desired outputs from LLMs. By understanding different prompting techniques, testers can leverage the power of LLMs to automate mundane tasks and free up time for more strategic testing activities. The session outlined various prompting techniques that can be applied to testing workflows, including Zero-Shot Prompting, Few-Shot Prompting, and Chain of Thought Prompting.

It was demonstrated how Prompt Engineering can be utilized for a variety of testing tasks, including generating test scenarios, formatting test cases for Jira, learning about testing topics, analyzing test cases, and generating test data.

It is important to note that the article emphasizes that Prompt Engineering is not a replacement for testers’ expertise. Instead, it serves as a powerful tool to enhance their capabilities. Testers who master Prompt Engineering can streamline repetitive tasks and dedicate more time to activities that require human judgment and creativity, such as designing effective test strategies and crafting comprehensive test reports.

Rahul concluded that Prompt Engineering offers exciting possibilities for testers to elevate their craft and contribute more significantly to the software development lifecycle. By embracing this new approach, testers can become more efficient and strategic, ensuring the quality and reliability of software applications. Watch the video.

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Bitol is a Linux Foundation AI & Data Sandbox project licensed under the Apache 2.0 license. As of now, it defines an open standard for data contracts called Open Data Contract Standard.

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