In this week’s In-Ear Insights, Katie and Chris discuss how the software development life cycle (SDLC) applies to prompt engineering in generative AI, why the prompt development life cycle (or prompt engineering life cycle) is a good idea, and a real-life application of it.

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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn: This week on In-Ear Insights, let’s discuss prompt engineering and software development—two seemingly disparate concepts that surprisingly converge. Here’s why: programming involves writing a sequence of text instructions for a machine to produce a repeatable, reliable result. You write code in C++, Python, or Java to perform a task and generate an output. Ideally, good code yields good results.

When you write prompts for generative AI and large language models, you’re essentially programming, just in a language like English, French, or Danish instead of C++. This means adopting good software engineering practices becomes crucial. You’re writing code, so you should protect it, audit it, and manage it effectively.

Katie, you’ve managed development teams—including me, bless your heart—when it comes to prompt engineering, development, internal sharing, and debugging. Many people, especially marketers, seem to just wing it instead of having a scalable, structured approach. So, when managing the prompt development lifecycle, what should people consider?

Katie Robbert: Requirements are paramount. Let’s take a step back. I was just checking our website for our version of the SDLC (software development lifecycle). We probably have it somewhere, maybe as an In-Ear Insight.

Chris, this topic arose last week when you drew a parallel between the SDLC and prompt engineering, highlighting the significant overlap. The question is, can we adapt the SDLC for prompt engineering? Absolutely!

The SDLC, like any project management or development lifecycle, has a few key phases: requirements gathering (business goals, development goals, needs, and approach), process definition (tools and methods), execution, testing, and iteration. It aligns surprisingly well with the five Ps framework: purpose, people, process, platform, and performance.

In this context, the purpose is to write a prompt for a specific task. People involve gathering stakeholder requirements. Process encompasses the prompt’s content, research needs, iterative testing, and validation methods. Platform seems straightforward—the environment for writing the prompt—but it might also involve data extraction from various platforms, feeding back into the process. Finally, performance measures the success of the task. While the SDLC is well-established in development communities, the five Ps framework provides a versatile lens.

There’s substantial overlap