5 Mistakes We Made Learning AI Development

Lessons learned the hard way so you don't have to.

Every methodology is born from mistakes. Here are the five biggest ones we made learning to build with AI—so you don't have to repeat them.

Mistake #1: Starting Without Architecture

Our first attempts with AI coding went straight to implementation. "Just build the login screen." The result? Technical debt within hours. AI excels at implementation but has no concept of system design. Always architect first.

Mistake #2: Not Managing Context

We'd start a conversation, build something great, then lose everything when the session ended. Context is king. We now maintain detailed documentation that provides AI with everything it needs to maintain consistency.

Mistake #3: Trusting Without Verifying

AI generates confident code. Confident doesn't mean correct. We shipped bugs because the code "looked right." Now every piece of AI-generated code goes through quality gates: lint, test, review.

Mistake #4: Fighting The Model

When AI suggested approaches different from our preferences, we'd fight to force our way. Sometimes the AI's approach was actually better. Learn when to guide and when to listen.

Mistake #5: Ignoring The Human Element

In our rush to maximize AI output, we forgot that humans still need to maintain and extend the code. Readable, well-documented code beats clever code every time.

The Meta-Lesson

AI development is still development. The fundamentals—architecture, testing, documentation, code review—matter more than ever. AI just lets you move faster, which means you can make mistakes faster too.