I’ll be honest I used to smirk whenever someone claimed AI would replace software engineers. The whole “vibe coding” thing seemed like hype to me. I stuck to using AI tools for learning and understanding technologies related to my current projects, nothing more.
That skepticism changed when my employer forced me to try Claude Code. Oh man, this thing is on another level. The output quality, accuracy, and produced code genuinely impressed me. It wasn’t just generating boilerplate anymore it was writing thoughtful, contextual solutions.
After using Claude Code for several weeks, I’ve developed a workflow that maximizes its effectiveness:
1. Goal Setting and Context I start by telling Claude Code about my goals. If I’m working on a team task, I’ll share the Jira or Linear ticket link and ask Claude to examine the requirements thoroughly.
2. Information Gathering I explicitly tell Claude to ask me questions if they need additional information. This back-and-forth ensures we’re aligned on requirements before any code gets written.
3. Implementation Planning Here’s the crucial part: I ask Claude to create an implementation plan without writing a single line of code yet. This step is where the magic happens.
4. The Critical Review Phase This is where most developers go wrong. During the implementation plan phase, I meticulously review everything Claude proposes. I question approaches, argue with implementation decisions, and tell Claude when suggested code isn’t relevant or needs adjustment.
5. Implementation Only after I’m satisfied with the plan do I ask Claude to start implementing.
Here’s the question that hit me after months of this workflow: What’s the most important step, and why can’t AI handle this alone?
The answer is obvious once you see it: the code review and analysis phase.
AI can generate impressive code, but it can’t evaluate whether that code fits your specific context, follows your team’s patterns, or addresses the real problem you’re trying to solve. That’s where your expertise becomes irreplaceable.
Having solid code review skills isn’t something you pick up overnight. It takes hours, days, and years to master. You need to understand:
Learning code review fundamentals becomes even more critical when working with AI:
Pattern Recognition: You need to spot when AI suggests patterns that don’t fit your codebase or introduce technical debt.
Security Awareness: AI doesn’t inherently understand your application’s security model or compliance requirements.
Performance Understanding: AI might suggest elegant code that performs poorly at scale.
Domain Knowledge: Only you understand the business context and edge cases that matter for your specific application.
AI coding tools like Claude Code are incredibly powerful, but they’re amplifiers of your existing skills, not replacements for them. The developers who thrive in this new landscape will be those who can effectively review, critique, and guide AI-generated solutions.
Your ability to catch edge cases, spot security issues, and ensure code fits your specific context that’s what makes you irreplaceable. AI can write code, but it takes human expertise to write the right code.
Invest in your code review skills. In an AI-powered development world, they’re not just useful they’re your competitive advantage.