Six Lessons From a Year of AI Coding (Most of Them Learned the Hard Way)

Over the past year, like a lot of people in our space, I've been experimenting with AI coding. And like any new skill, I've learned far more from the failures than the wins. I've been diligent about examining what worked, what didn't, where the major pitfalls live, and what consistently drove the best results.

I'm a strong believer in sharing what you know to help the broader community. That's been true my whole career in the channel, and it's true here. So here are the top lessons I've learned building real products with AI, not just demos.

1. Plan First, Build Second

Start every project by telling your AI coder not to write a single line of code until you say go. These tools are built for user satisfaction, and the way we operate as a society today is as fast as possible. Your coder will instantly want to pump out something impressive to show your idea off. It feels great in the moment. From an actual usability standpoint, it won't get you where you need to go.

I spend days, sometimes weeks, whiteboarding an idea before any code exists. Architecture, data model, user flows, edge cases. The AI is a phenomenal thinking partner during this phase, and it costs you nothing but time. Rushed foundations cost you everything later.

2. Learn From Other People's Mistakes

Do the research on where others failed before you start. If you want to whip up a quick web form or landing page, sure, jump right in. But if you're building a real product, especially one for resale, you need serious time invested in the right foundation.

I spent months reading through articles, social media posts, and courses from Anthropic and OpenAI to understand the biggest pitfalls people hit during their builds. I saved hundreds of these lessons into a single document, and now every build I do starts with the AI reading that document before it writes anything. It's become one of my most valuable assets. The mistakes are out there, documented, free. You just have to go collect them.

3. Build for the Outcome, Not the Idea

I know this one sounds simple, but it's easy to tell an AI coder "I want to build a ______" and stop there. Over time that idea will evolve. New features, new functionality, new integrations. All of that happens naturally as your familiarity with your own product grows, and all of it is fine, as long as it builds toward a singular goal.

This is where building toward a solution instead of a feature becomes critical. Adding features over time is doable. Adding foundational pieces later is not. If your product needs to be multi-tenant, that has to exist from day one. Retrofitting it is a rebuild, not a feature.

Before you start, imagine the finished product with a real user inside it. Describe every piece. What types of users exist? Are there different levels? Permissions? What does the data look like when there are a thousand records instead of ten? Answer every one of those questions and hand the answers to your AI. If you don't know what you're building toward, neither does the AI. It will guess, and that is where your build dies.

4. Security Is Not a Feature You Add Later

This is the one that separates a toy from a product. AI coders will happily ship you an app with API keys sitting in frontend code, tokens stored in localStorage, and endpoints anyone can hit without logging in. It will look finished. It will work in the demo. And it will be a liability the moment a real user touches it.

I built a standing security checklist that gets applied before anything ships: no keys in the frontend, rate limits on login, server-side protection on every route, proper password hashing, generic error messages, the whole list. It runs on every project, every time, no exceptions. The AI doesn't push back on any of it. But it will not volunteer any of it either. You have to bring the standard, because the tool won't.

5. The Smallest Details Cause the Biggest Failures

Here's a real one. I had a core feature in one of my builds silently fail, and the root cause was a single character. Part of the system stored a value using a hyphen, another part used an en-dash. Visually identical to a human. Completely different to a database. The feature returned nothing, threw no errors, and looked perfectly healthy.

The lesson isn't about punctuation. It's that AI-generated code across multiple sessions will drift unless you enforce consistency. I now maintain a set of standing rules that get restated at the start of every session: formatting conventions, naming patterns, non-negotiables. The AI has no memory of the standards from three sessions ago unless you give it that memory. Consistency is your job, not the tool's.

6. You Are the Architect, the AI Is the Builder

The single biggest unlock in my workflow was separating design from execution. I write the specification first: what gets built, in what order, with what constraints. Then I hand the spec to the AI in scoped sessions, each with a clear start and finish. Big ambitious sessions produce sloppy, half-finished work. Tight scoped sessions produce clean output you can actually verify.

And verify you must. I review the output of every session before the next one starts. I apply my own database changes by hand. I test the flows myself. The AI is an unbelievable force multiplier, but it is not accountable for the result. You are. Treat it like the most talented contractor you've ever hired: give it great blueprints, inspect the work, and never assume.

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A year in, my honest take is this: AI coding is real, and it's the closest thing to a superpower I've picked up in 15 years in this industry. But the people getting real products out of it aren't the ones prompting the fastest. They're the ones planning the hardest.

If you're in the middle of your own build and hitting walls, my inbox is open. We all get further when we share what we've learned.