Thoughts on AI-Assisted Development

We're living through a fascinating time in software development. AI coding assistants have gone from novelty to necessity in just a few years. Here are some thoughts on how they're changing the way I work.

The Good Parts

1. Faster Iteration

The biggest win is speed. Tasks that used to take 30 minutes now take 5. Need to write a regex? Generate boilerplate? Convert data formats? AI assistants handle these mundane tasks instantly.

# Me: "Write a function to validate email addresses"
# AI: *generates working solution in 2 seconds*

import re

def is_valid_email(email: str) -> bool:
    """Validate an email address using regex."""
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return bool(re.match(pattern, email))

2. Learning Accelerator

AI assistants are incredible for learning. You can ask "why" and get explanations. It's like having a patient senior developer available 24/7.

"The best way to learn is to build things and ask questions along the way."

3. Documentation Helper

Writing docs is often the first thing to get cut when deadlines loom. AI can generate decent documentation from code, making this task much less painful.

The Tricky Parts

1. The Confidence Problem

AI assistants are confidently wrong sometimes. They'll generate code that looks perfect but has subtle bugs. You still need to understand what you're shipping.

2. Over-Reliance Risk

There's a real risk of becoming dependent. Skills atrophy without practice. I try to solve problems myself first, then use AI to refine or speed up.

3. Context Limitations

AI doesn't know your codebase's history, your team's conventions, or why that weird workaround exists. Human judgment remains essential.

My Current Workflow

Here's how I've integrated AI into my development process:

1. Start with intent: Write comments describing what I want to build
2. Generate, don't copy: Use AI suggestions as a starting point
3. Review everything: Treat AI code like any other PR—review it carefully
4. Learn from suggestions: When AI does something clever, understand why

Looking Forward

I think we're still in the early days. The tools will get better, and so will we at using them effectively. The developers who thrive will be those who can:

• Clearly articulate what they want
• Evaluate code quality critically
• Integrate AI help without losing core skills

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What's your experience with AI coding assistants? I'd love to hear how others are adapting their workflows.