Most developers are using AI wrong and they don’t even realize it.
They either think AI is magic that’ll write their entire codebase or they dismiss it completely because “it makes mistakes.” Both camps are missing the point entirely and yeah I’ve been on both sides of this so let me break down what actually matters.
Here’s the thing - AI is a skill. Not magic. Not a replacement. A skill. Every developer knows how to use a database right? You learned SQL, you understand indexing, nobody calls that “cheating.” AI is exactly the same and the sooner you treat it that way the sooner you stop falling behind.
1. Your Codebase Structure Matters More Than Your Prompts
Okay so this is probably the most underrated insight - AI agents don’t thrive in garbage code.
Messy dependencies? Unclear boundaries? Inconsistent patterns? The AI will struggle with your codebase just like a new team member would on their first day. I’ve seen this firsthand - take the same AI tool, point it at a well-structured project versus a spaghetti codebase, and the results are night and day.
Clean architecture isn’t just for humans anymore. It’s the foundation that enables AI to actually help you. If you’re wondering why Claude or Copilot keeps giving you weird suggestions maybe the problem isn’t the AI - it’s your code structure telling it the wrong story.
The takeaway: Before you blame the AI for bad output, look at what you’re feeding it. A well-organized project with clear patterns gives the AI context to work with. Garbage structure gives it garbage context.
2. AI Is Not a Mind Reader
You can’t just say “make it better” and expect miracles.
The AI doesn’t know your business context. It doesn’t know your constraints. It doesn’t know that your team prefers composition over inheritance or that your PM will reject anything that changes the existing API contract.
All right so every time you prompt an AI agent you need to communicate these things clearly. Your requirements, your constraints, your preferences - every single time. The developers who get the best results aren’t writing better prompts, they’re communicating better context.
Think about it like onboarding a new developer. You wouldn’t just say “fix the bug” without explaining the system, the constraints, and what a good solution looks like. Same thing with AI.
3. Planning Is Everything - Garbage In, Garbage Out
This one sounds obvious but almost nobody actually does it.
Before you even open your AI tool you should know what you’re building. What’s the expected behavior? What are the constraints? What patterns should the code follow? What’s the definition of done?
A fine-tuned plan is the difference between magic and mess. I’ve watched developers spend 3 hours wrestling with an AI agent because they skipped 10 minutes of planning. The AI generated code fast, sure, but it was the wrong code going in the wrong direction and they kept course-correcting in circles.
The pattern that works:
- Define the expected behavior first
- List your constraints explicitly
- Specify which patterns and conventions to follow
- Break complex tasks into clear steps
Then hand it to the AI. The output quality difference is massive.
4. You Cannot Outsource Your Judgment
Okay so this is the most important one - judgment is the most critical skill in the AI era.
AI generates code fast. Really fast. But it doesn’t know if that code is right for your situation. It doesn’t know about your team’s velocity, your deployment deadline, your users’ edge cases, or the tech debt you’re trying to pay down.
The AI can propose 5 different architectures for your feature. Only you can decide which one fits. That requires understanding your system holistically - the parts the AI can’t see.
And yeah this is why senior engineers are getting more productive with AI while juniors sometimes get stuck in loops. It’s not about prompt engineering. It’s about having the judgment to evaluate and direct the output.
5. Your Experience as an SE Matters More Now, Not Less
Here’s something people get wrong - they think AI levels the playing field. It doesn’t. It amplifies the gap.
Same AI tools. Same prompts. Give them to a junior dev versus a senior engineer and you’ll get completely different results. The difference? Judgment. Taste. Experience. Knowing what good code looks like, knowing when to push back on a suggestion, knowing when the AI is confidently wrong.
You’re not coding anymore - you’re engineering what you want. The AI doesn’t care about clean code or architecture unless you care about them. You’re the one setting the standard and the AI is executing to that standard.
6. Vibe Coding Has a Ceiling
All right so let’s talk about vibe coding because everyone’s doing it and nobody talks about when it breaks down.
Vibe coding is fine for prototypes and validation. You want to test an idea quickly? Go for it. Let the AI generate, don’t worry about structure, just validate the concept.
But you will hit a wall. You’ll feel it. Can’t add features without breaking something else. Can’t fix bugs because you don’t understand the generated code. Everything’s tangled together in ways that made sense to the AI in isolation but make zero sense as a system.
That’s your signal to step back and structure the project properly. And that’s exactly where your experience as a software engineer becomes your superpower. A vibe coder without SE experience hits that wall and is stuck. You hit that wall and you know exactly what to do - refactor, establish patterns, create boundaries.
7. Go Wide While AI Goes Deep
AI can drill into one problem with incredible depth. It can analyze a function, trace dependencies, suggest optimizations - all focused on one piece of the puzzle.
What it can’t do is see your whole system. It can’t see the tradeoffs between your deployment strategy and your database schema. It can’t weigh the impact on team velocity when choosing between a quick fix and a proper refactor.
This is where T-shaped understanding matters. You need enough depth to verify the AI’s output - to look at generated code and know if it’s correct. But you also need enough breadth to see how that output fits into the bigger picture.
The vertical: depth in your craft. The horizontal: breadth across the system. AI handles the vertical better than ever. The horizontal is all you.
The Bottom Line
AI doesn’t replace engineering skill. It amplifies it.
Good judgment + AI = exceptional output. No judgment + AI = faster garbage.
The developers who’ll thrive aren’t the ones writing the fanciest prompts. They’re the ones with solid fundamentals, clean codebases, and the judgment to direct AI tools effectively.
Learn the tools. Master the fundamentals. This is your era.
ciao