Almost Timely News: 🗞️ My Biggest Generative AI Win (2025-06-29)

Dip your toes into the coding pool

Christopher S. Penn

Jun 29, 2025

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What's On My Mind: My Biggest Generative AI Win

This past week, as part of my promotional tour for my new book, Almost Timeless: 48 Foundation Principles of Generative AI, I hit the podcast circuit. One of the questions I was asked was fascinating to think about, and in the moment, the answer I gave was correct but not thorough, so we'll remediate that today.

The question was: what's your biggest win with generative AI?

Generative AI is so incredibly capable of so many tasks across so many domains that it's often difficult to think of any one singular thing that represents a massive win, but in the heat of the moment, the first thing that sprang to mind was software development.

Why? Because generative AI is absolutely superb at the act of generating software. It can program in any major coding language, and today's models are so incredibly smart that they can pretty much accomplish anything you tell them to.

However, they're also prone to tons of mistakes, of going off the rails, of getting caught in endless loops. So today, let's talk through HOW I use generative AI to construct software.

Now, before we continue, you might say, but Chris, I'm not a software developer. I'm not a coder. I don't know how to code.

Neither do I, not really. I'm a pretty crap coder. I'm mediocre, mid at best on a good day. But I am a fabulous question asker and a solid thinker. I know enough of the lingo to prompt generative AI well, and I have generally good ideas. That combination of attributes is, to me, the best combination for AI + humans: AI brings the manual labor, I bring the ideas and direction. In a lot of ways, it's management and delegation. I'm not doing the typing, but I am absolutely managing the project.

So let's dig into how I do this, and perhaps you'll pick up some things you can use yourself. If you want to try this out and you don't want to install things yourself, you can always copy and paste your code into a free environment like Google Colab.

Part 1: The 5Ps

Everything begins with Katie Robbert's fantastic Trust Insights 5P Framework:

Purpose: why are we building this software?
People: who is going to use it and what are their needs and limitations?
Process: how are we going to build it?
Platform: what language and toolings are we building it in?
Performance: how do we know we succeeded?

Let's pick a toy example for today, something we can easily put together as a simple example. Let's say I want to take an export of my YouTube videos from my channel and categorize them, then make a viewer's guide of videos, like a cheat sheet of my content. That's a fairly straightforward task save for one thing: I have hundreds of videos on my channel, which means that I can't just copy paste the entire thing into a generative AI tool and have it spit out a clean result.

Especially if I want to group things and categorize them, make them neat and orderly. So I could export the raw list of data from my YouTube channel, and then we need to create some code to process it, make decisions about how to classify my videos, and ultimately spit out a nice looking guide that I can share with you as a form of bonus content.

I'd determine the purpose with the creation of a user story, something like this:

As a content creator on YouTube, I need to create a cheat sheet of categorized YouTube videos since January 2024 so that my viewers can find exactly the videos they want to see quickly.

User stories fit in the format of "As a {role}, I need to {task} so that {outcome}" - a very handy way to pick apart many of the requirements for our project. You can learn a lot more about this in Katie's AI-Ready Marketing Strategy Kit, free of charge (form fill required) here. It's not a stretch at all to say that almost all of what I've learned about great planning over the last 10 years, I've learned from her.

The people are pretty clear: you, my audience. I want you to find exactly what you're looking for quickly.

The process... is what we're doing. Broadly, my software needs to ingest a CSV file, infer the correct YouTube URLs, classify the videos by the topic using fuzzy keyword matching in a set of topics we have to determine with generative AI, and then produce a nice guide in HTML and CSS of my videos, organized by topic, by date in descending order, with the video titles all linked back to the source videos.

The platform is going to be Python to generate the outcome. The outcome itself will be simple, styled HTML that looks nice. I choose Python, not because I love it (I really don't, I'm old and prefer more C-like languages), but because it's the language that generative AI speaks the absolute best. I might also specify additional key details like the software must be completely self contained and rely on no external APIs or services. It must operate without an Internet connection of any kind.

And the performance is a working piece of software, free of bugs, that successfully generates the cheat sheet.

Here's the secret of Katie's 5P framework: it dramatically shortens requirements gathering because we just did about 75% of it.

Part 2: Best Practices and Coding Standards

Okay, so we've gotten our 5Ps done. Next, we need coding standards and best practices. You can't - well, you shouldn't - just go to your favorite generative AI tool and say, "Hey, let's code this thing!". That will result in code spaghetti, massive technical debt, and... well, flat out disaster.

Instead, we want to use generative AI's deep research tools - available in pretty much every platform - to build us a best practices guide, starting information that we'll use with generative AI to build our code and guide us in the process.

Here's an example prompt that we would use, based on the 5Ps we just did.

I need to construct a best practices guide for Python 3.11 to accomplish building this software. The guide should contain best practices for Python 3.11 for the specific purpose of the software, should include lists of libraries and packages and their current versions that would make development of the software easy and fast, should rely solely on free, open source software (FOSS), and should contain code samples and examples. It's 2025-06-28; prefer sources from 2024-2025.

I then copy and paste the 5Ps from above as the second half of the prompt and feed it to my deep research tool.

Let the deep research tool assemble the custom best practices guide, which will take anywhere from 5 to 50 minutes depending on the tool you use.

Why do this? Because every project is different, and we want to leverage the knowledge out there that deep research tools can assemble specific to this use case. While having general, global coding standards is good, having coding standards that fit the specific project can be beneficial - especially when the deep research tools surface things like packages and libraries that you didn't even know exist.

You can see an example of the coding standards for this project in the video version of the newsletter, as it's quite extensive.

Part 3: Requirements Gathering

We have our 5P framework and our coding standards and best practices. It's now time to do requirements gathering. The outcome of this stage is something calle a product requirements document, or a PRD. This is something software development teams use as the guidebook or rules for a project, and contains everything that the project should have.

A great PRD can be picked up by any product or project manager and they'll instantly know what's supposed to be happening, with no other context besides the PRD. It should be that thorough and self-contained.

Here's the prompt I use:

Let's build a product requirements document for a Python app called YTGuide, a pun of TVGuide. I'll provide some background information first based on the Trust Insights 5P Framework and our overall coding standards and best practices for this project. Read through the background information, then ask me one question and ONLY one question at a time until you have enough information to build a PRD. Remember that PRDs contain user stories, functional requirements, non-functional requirements, and domain requirements. They are fully self contained and have all the information a software development manager and team need to construct work plans and begin coding once complete.

Then plan to spend up to an hour answering questions, depending on how thorough your 5Ps and coding standards documents are.

Once you're done, your generative AI tool should spit out a very nice, very lengthy PRD. You'll need to save this and store it for the next step; I recommend saving it as a Markdown file (a special kind of plaintext file that AI tools easily read, has a .md extension).

Part 4: Workplans

The PRD alone isn't enough to build from, not if we want things to go well. Using the smartest model you have access to, we need to convert the PRD into incredibly granular, detailed instructions, a work plan.

A work plan should be a file-by-file, blow-by-blow set of instructions that either AI or a human programmer could follow. It's so detailed and rich that it's almost copy-paste instructions for human or machine to build our app for us. Here's the prompt I typically use:

Based on our PRD, create a workplan to build the Python app from the PRD. The workplan must be a highly detailed set of instructions, file by file, for an unskilled, junior developer to use to update the codebase while adhering to our coding standards. The files should be ordered from fewest to most dependencies, and must contain the file path, the changes needed, the upstream and downstream dependencies to keep in mind, and the overall intent of the file. The workplan must be granular enough that the junior developer needs no other information to make the necessary code. There must be no references to previous versions or documents; the workplan must be completely self-contained. Include diff code samples appropriate for use with systems like git. Build the detailed workplan in Markdown.

Your favorite generative AI tool will spit out the workplan; as with the PRD, save this in Markdown format.

Part 5: The Build

Now, after everything we've done so far in generative AI, we're ready to hand this off to AI to build. There are so, so, so many AI coding agents available today - OpenAI Codex, Claude Code, Gemini Code Assist, etc. and many of them are either free or very low cost to use.

What we will do is make a folder on our computers called ytguide, and in that folder make a subfolder called docs. Inside that folder, we're going to put:

prd.md - our requirements document
workplan.md - our workplan
standards.md - our best practices and standards guide

For this project, I'll be demoing Gemini Code Assist, but you can use any tool you like. It really doesn't matter which.

What we do at this point is start our coding tool and give it clear instructions about what to build and how to build it. We've already created all the details in our supporting documents, so this really is more of just a nudge.

Here's the standard prompt I typically use:

Read @docs/standards.md to understand our coding practices. Read the product requirements document at @docs/prd.md to understand the overall intent of the project. Implement the workplan strictly as specified in @docs/workplan.md exactly as detailed, from start to finish, following the plan. Do not introduce any undocumented changes or execute any changes/additions that are not documented in @docs/workplan.md. Follow instructions strictly, implementing diffs precisely as shown. When done, create a log called @docs/progress.md and update your progress as to what you've accomplished. Remember that you are not done until you've run ruff, mypy, and pytest and all tests are free of errors. If you run these tests and they generate errors, you must fix the errors.

This gives the coding agent a concise, swift kick in the pants to get underway. Once you're sure it's running, go take a break.

Part 6: Debugging

Rarely will any software, human or AI, run perfectly without errors on the first try. Often, things go wrong, sometimes hilariously wrong. This part of the process is all about debugging. We want our AI tool to do the debugging for us. Here's one of the prompts I use to do debugging; this just gets pasted right into your coding agent:

Evaluate the code versus the @docs/prd.md, @docs/workplan.md, and @docs/standards.md. What's done, if anything? What's not done, if anything? Think in terms of QA, looking for root causes, underlying problems, systemic issues. ✅ What's good, if anything? If there's nothing good, state so. ❌ What's bad, if anything? If there's nothing bad, state so. ❓ What's missing, if anything? If there's nothing missing, state so. 🗑️ What's unnecessary, if anything? If there's nothing unnecessary, state so. 🛠️ What's fixed, if anything? If nothing was fixed, state so. 💥 What's newly broken, if anything? If nothing was newly broken, state so. Don't code. Don't write code. You are evaluating and analyzing only. Do not code.

Your AI tool should come up with a solid analysis of what's going on. From there, simply tell it to fix the issues it has identified. This multi-step debugging is important - we need to constantly refresh AI's mind about what it's supposed to be doing so it doesn't get caught up chasing its own tail.

Once you've done this process and your code works, you can enjoy the fruits of your labor, like a YouTube channel guide you can publish for your audience that's tailored to their needs.

Part 7: Wrapping Up

While this is admittedly still a decently technical process, you can see that I don't do much coding at all here. Instead, I am making corrections. I am guiding. I am managing the process, and AI is doing all the heavy lifting to not only code, but help me design, ideate, test, and deploy.

What's more, if I change my mind about something, I can edit the requirements document with AI's help and then repeat the process. It'll make all the necessary changes to the code - I just have to be absolutely clear about what the idea is, about how I want AI to implement it.

The reason this is my biggest win with generative AI is that it's not about AI at all. It's about what AI does for me, how AI helps me do my job better. By using AI to create software, I am doubling down on the secret sauce of my work, building custom tooling that helps me do my job better than ever, doing it my way, doing it with my particular point of view.

This isn't using AI to create a watered down, generic work product. If anything, this is doing the opposite, creating a distilled version of my point of view for this work and turning it into tools I can use to scale my work, my ideas, my capabilities.

I'd also point out that while I did this with software, there's nothing stopping you from using this general idea and methodology for ANYTHING that requires planning. Build your quarter by quarter content strategy. Implement custom versions of analytics dashboards you've always wanted but no vendor ever made. Write a book with this set of techniques - the sky's the limit.

AI brings the skills - you bring the ideas.

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