5 Programming Secrets Learned The Hard Way (That AI Still Can't Teach You)

We're living in a surreal time. If you’re not using AI (GitHub Copilot, Gemini, ChatGPT) to write at least some of your boilerplate, you're already behind.

But relying on a model, no matter how powerful, has revealed new, brutal truths about development.

Here are five "secrets" I learned the hard way—lessons that define the difference between a great engineer and a great prompt engineer.

1. 🛑 Secret #1: The Error Message Is The Real Product. AI is fantastic at generating code that looks right. But the moment that code breaks, the true test of engineering quality begins.

The AI Problem: Generative models prioritize smooth, functional-looking code. They don't prioritize debuggability. Their generated errors are often generic, context-free, and lead you on wild goose chases.

The Hard-Won Secret: In complex systems, the most valuable code you write is not the happy path—it's the error handler. Design your code, logs, and exceptions so that when something fails, the error message gives the next developer (Future You, or an SRE) the exact context they need:

Where the failure occurred.

What the input state was.

Why the system believes it failed.

The Vibe Check: Good vibe coding means your code is so debuggable, you barely even need to reach for the debugger.

1. 🤯 Secret #2: Never Trust the First Output from Your Prompt. AI outputs code that is statistically plausible. It’s a shortcut to a solution, not a guarantee of correctness or security.

The Hard-Won Secret: The real value of AI is scaffolding, not completion. Every single time I used the first AI-generated block of code without modification, it introduced a subtle bug—a resource leak, an insecure dependency, or a slow query—that took longer to track down than if I had just written it myself.

The Vibe Strategy: Use AI to generate three different approaches (e.g., "Write this in a functional style," "Now write it using a loop," "Now write it using a specific library"). Then, hand-synthesize the best, safest, and most idiomatic parts into your final, human-vetted code block. This forces you to engage your critical thinking, which is the skill AI cannot automate away.

1. 🧘 Secret #3: You Don't Understand It Until You Can Remove It. Complexity is the silent killer of scalability and maintainability. In the age of AI, we get complex code faster than ever.

The Hard-Won Secret: The moment I truly understood a module, a dependency, or a design pattern was the moment I realized I could delete or simplify a significant portion of it without losing functionality.

The Vibe Coding Principle: Good vibe coding is Subtraction. If an AI suggests a 50-line utility function, your job is to check if the same result could be achieved with a 10-line native language feature. Minimize the surface area of your codebase—fewer lines of code mean fewer lines that can hide a bug.

1. 🔗 Secret #4: The Dependency Is The Debt. It's tempting to use an AI prompt like, "Generate a solution for X using Library Y." You get instant code, but you just accepted a massive, unlisted liability.

The Hard-Won Secret: Every single external dependency you add (a library, a framework, a third-party API) is unsecured technical debt. You are trusting an external team to maintain security, versioning, and compatibility on your behalf, forever.

The Vibe Strategy: Before running npm install or pip install, ask: "Is this feature so complex that the maintenance cost of the dependency is lower than the cost of writing 50 lines of simple, native code?" Often, the answer is no. Choose simple, widely-vetted dependencies, and be ruthless about auditing the rest.

1. ✨ Secret #5: Context Over Cleverness. Every Single Time. AI is clever. It can write intricate, abstract, and highly optimized snippets of code.

The Hard-Won Secret: When debugging a catastrophic production failure, no one ever says, "Wow, that lambda function was so clever!" They say, "Why is this so hard to read?"

The Vibe Principle: Contextual Clarity Wins. The code must clearly communicate the business intent behind the technical implementation. If your code requires 15 lines of abstract logic to save 2 milliseconds, you have made a poor trade-off. Choose the code that the next human developer will immediately grasp, even if it feels slightly "less optimal" on paper.

What is the biggest mistake you've caught from an AI-generated code block? Or what's a non-technical "vibe" principle you live by when writing code?