In this week’s In-Ear Insights, Katie and Chris talk through how to improve your prompt engineering for large language models like ChatGPT, GPT-4, and other services through the use of the software development lifecycle. Learn how to apply the SDLC to your individual work with AI tools, and why it’s so important.

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In this week’s In-Ear Insights, we’re talking about prompt engineering, which is the art and science of writing prompts to talk to large language model AI systems, and how prompt engineering is really actually, software development.

This past weekend, I was doing a bit of ruminating on it, and realizing that with things like Microsoft copilot, and Google palm and GPT, four, and all the galaxy of acronyms out there, when we roll out the ability for a, an office worker to talk to PowerPoint, or Excel with a prompt, and access to GPT, for the large language model and have it do something like create a PowerPoint presentation from this Excel spreadsheet, we are really talking about how a human being talks to a computer, right how human being gives instructions to a computer, which is software development.

So the art of prompt engineering is how we write those programs to talk to computers.

And now that this is coming to Microsoft Office and Google Docs and stuff.

Everyone, every employee who works in office productivity software, is going to become a software developer, right? Because when they write prompts, they’re writing software.

So Katie, I want to ask you about the software development lifecycle and how when you think about prompt engineering, how it’s going to evolve.

So as a refresher, for folks who don’t remember, the software development cycle looks like this.

Okay.

Do you want to step through this real quick?

Yeah, absolutely.

So the software development lifecycle is exactly that.

It’s a lifecycle.

And it’s meant to be repeated over and over and over again.

So you start with your purpose? What’s the question we’re