Building With AI Assistants: A Developer's Perspective

The conversation around AI in software development often gets framed as a binary: either AI will replace developers, or it's just a fancy autocomplete. Having spent the past few months deeply integrating AI assistants into my workflow, I've found the reality to be far more nuanced—and far more interesting.

The Augmentation Mindset

The most productive framing I've found is thinking of AI as a collaborator with a very specific skill set. It's exceptional at:

• Pattern recognition: Spotting common code patterns and suggesting implementations
• Translation: Converting between formats, languages, and paradigms
• Exploration: Quickly generating multiple approaches to a problem
• Documentation: Explaining code and generating docs from context

It's notably less good at:

• Understanding business context and tradeoffs
• Making architectural decisions that consider long-term maintenance
• Debugging issues that require deep domain knowledge
• Knowing when not to build something

My Current Workflow

Here's how AI fits into my daily development:

Morning: Planning & Research

I start most features by having a conversation with an AI assistant about the problem space. Not to get the answer, but to explore the solution landscape. What patterns exist? What are the tradeoffs? What am I not considering?

// Example prompt pattern I use for exploration:
// "I need to implement [X]. My constraints are [Y].
// What are 3-4 different approaches, with tradeoffs?"

This conversation often surfaces considerations I wouldn't have thought of on my own.

Midday: Implementation

During implementation, I use AI for:

1. Boilerplate generation: Setting up test files, component scaffolds, API routes
2. Rubber ducking: Explaining my approach and getting feedback
3. Quick lookups: "How does this library handle X?" is faster than docs sometimes

What I don't do: blindly accept generated code. Every suggestion gets reviewed, understood, and often modified.

Evening: Review & Documentation

AI excels at helping write documentation, commit messages, and PR descriptions. It can see patterns in my changes that I might miss after hours of deep work.

The Pitfalls to Avoid

Over-delegation: The temptation to let AI handle everything leads to code you don't understand. When bugs appear—and they will—you'll be lost.

Context collapse: AI doesn't know your codebase's history, your team's conventions, or why that weird workaround exists. Always provide context, and always sanity-check against your domain knowledge.

Speed over quality: AI makes it easy to generate a lot of code quickly. But more code isn't better code. Sometimes the best solution is deleting code, and AI will rarely suggest that.

What I'm Optimistic About

The most exciting aspect isn't the current capabilities—it's the trajectory. I'm particularly interested in:

• Codebase-aware assistants: Tools that understand your entire project, not just the current file
• Test generation: AI that can reason about edge cases and generate meaningful tests
• Code review assistance: Catching issues before they become bugs

Conclusion

AI assistants are tools, and like any tool, their value depends entirely on how you use them. The developers who will thrive aren't those who resist AI or those who over-rely on it—they're the ones who learn to collaborate with it effectively.

The skill isn't "knowing how to code"—it's knowing what to build and why. AI can help with the how; the what and why remain deeply human.

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What's your experience been with AI in development? I'd love to hear different perspectives—reach out on LinkedIn or email.