Prompt Playbook: Building your first AI App PART 1

First builder - let's call her Sarah - proudly showed me her preparation work.

It was comprehensive! She'd spent three months studying Python (despite having no coding background), watched about 20 hours of prompt engineering theory videos, and had the most elaborate Notion dashboard I've ever seen, filled with API documentation and architecture diagrams.

But no working tool.

Then there was Mike.

Pulled out his laptop, opened up a basic AI interface, and started testing prompts for his HR consulting problem. Rough as anything, but by the end of the day, he had something that could actually help with job descriptions.

Mike's launched three basic AI tools. His first one was rough - absolutely basic stuff, looked like it was built in about 20 minutes (because it was). But here's the thing - it was DONE. It worked. Sorta.

And because it was done, he could show it to people. Get feedback. Iterate. Now his third tool actually looks half decent and, more importantly, he's got paying customers.

Meanwhile, Sarah messaged me asking for recommendations on courses about large language model architecture because she wants to "really understand the fundamentals" before starting.

I'll take done over perfect every single time.

Perfect never happens. Done does.

Look, I get it. The temptation to learn everything first is strong. Especially if you've come from a traditional tech background where you need to understand all the moving parts before you build anything.

But here's the thing: it's 2024. The tools have changed. AI has seen to this. Our mindsets just haven’t caught up yet!

You don't need to understand how the engine works to drive the car. And you certainly don't need to know how to build an engine before you start driving. Just drive the damn thing.

That’s what we’re covering this week.

Let’s get started

Stop Learning, Start Building

The End of "Getting Ready to Start"

Here's what nobody tells you about building AI tools: most people never actually start. They get stuck in an endless cycle of preparation. One more tutorial. One more course. One more framework to understand.

It’s procrastination. It feels like we’re doing work. But it’s the wrong sort of work.

Basically, it's not about the knowledge - it's about the action. And action starts way before you think you're ready.

My first “big” business was co-founding a TV station in Vietnam. I was so young and naive (read: stupid!) that it didn’t dawn on me that starting a TV station in Vietnam wasn’t something you did.

So…it got done. 9 months after starting it up we were on air.

Just starting and working it out as you go along beats careful planning every time. When we hit problems we’d work out how to get around it - either by looking it up in a book (for real!) or asking experts who had done it before.

More recently I see this wonderful example from the world of video games from creator of Balatro, a game that's been nominated for Game of the Year.

The game's production folder is still sitting in their "Learning" directory. They didn't wait until they'd "graduated" from learning mode - they just started building. Now they've got one of the most acclaimed games of the year. They started before they were ready, learned while building, and shipped something incredible.

We need to break down the barrier between learning mode and doing mode.

You will always be in both. You never graduate from learning mode - you just start building while you're in it.

The Modern AI Advantage

The beauty of modern AI tools is that they've changed the game entirely. You don't need to front-load learning anymore. The tools themselves guide you. They teach you. They grow with you.

Hit a technical roadblock? No problem - you just ask AI how to get around it and it’ll give you the steps. Hell, it’ll even implement the fixes for you in some tools.

Think about it: ten years ago, building any kind of software tool meant years of learning. Now? The barriers have entirely dissolved.

Looking at Mike's journey, what made the difference wasn't his technical knowledge - it was his willingness to start before he felt ready.

His first tool was rough. Embarrassingly basic. But it existed. And that's the key difference.

While Sarah was planning the perfect architecture, Mike was

• Testing basic ideas

• Getting real feedback

• Learning from actual usage

• Improving based on reality

• Moving forward every day

The hardest part isn't the building - it's breaking free from the "I need to learn more first" mindset.

Stop asking

• "What do I need to learn?"

• "What if I do it wrong?"

• "What's the best way to structure this?"

Start asking

• "What's the smallest thing I could test?"

• "Who could I show this to?"

• "What's the fastest way to get feedback?"

What’s next

Over the next four parts of this playbook, we're going to build your first AI tool - fast. No fluff, no theory, just pure action:

Part 2: Preparing Your Base Prompt We'll use ChatGPT to craft and test your core prompt. One input, one output. No fancy stuff - just getting your basic flow working.

Part 3: Moving to a Simple Platform Time to give your prompt a home. We'll look at the easiest deployment options and get your tool online without any coding needed.

Part 4: Creating Clear Instructions We'll make sure your tool is crystal clear to use. Adding instructions, example inputs, and the knowledge it needs to work properly.

Part 5: Getting Ready to Share Basic testing and tweaks to make sure your tool doesn't fall flat when people try it. Making it share-worthy without overcomplicating things.

Remember: Mike's first tool looked like it was built in 20 minutes because it was. But three months later, he's got paying customers while Sarah's still getting ready to start. We need to get through that first 20 minutes! That’s the only goal this week!

Keep Prompting,

Kyle