How to maintain quality and velocity when AI assistants become part of your development workflow

“It is not that you have to TDD at the phase of line of code by line of code, but when things go wrong, you have to have the capacity to TDD at the phase of LOC by LOC.” – Kent Beck, TDD by Example

This advice, written decades before AI assistants existed, remains as relevant today as it was then. Last week, I experienced this firsthand when GitHub Copilot confidently generated what looked like perfect relative path handling code. It looked right. I run my Python script. It was completely wrong.

The issue? I’m not the only developer who struggles with relative paths. The AI’s training data likely includes countless examples of incorrect path concatenation – a common source of subtle bugs. What should have been a 10-minute task turned into 40 minutes of debugging, defensive programming, and test writing to identify and fix the issue.

The Amplification Effect

AI assistants don’t just accelerate good practices – they amplify everything. Teams with solid development disciplines adapt to LLM workflows naturally, quickly identifying where AI adds genuine productivity gains. Those without established practices find themselves debugging both their processes AND the AI’s suggestions.

The fundamental is not related to the AI, It’s ensuring your team maintains quality standards when the perception of increased productivity can hijack developers’ judgment.

Why Small Steps Matter More Now

The TDD advise still matters today, with AI coding assistants:

Small steps become critical because AI-generated code often looks deceptively correct. Large commits with AI assistance make root cause analysis exponentially harder when issues surface. Your ability to isolate problems to specific, small changes becomes your primary debugging tool.

Following a plan. Without clear objectives, AI assistance becomes productivity theatre. Stay focused on the requirements and on good design, review your AI recommendations with the project goals and knowledge of the fundamental principles of good design/programming. 

Strong safety nets – comprehensive automated testing, code review processes, and monitoring – catch AI mistakes before they reach production. The AI might generate syntactically perfect code that fails edge cases your tests should have caught.

The Discipline Paradox

Here’s the paradox many teams face: they adopt AI tools expecting to move faster, but without proper development practices, they actually slow down. The teams that successfully integrate AI assistants already had strong testing cultures, clear coding standards, and robust review processes.

This creates a compound advantage. Teams with good fundamentals get an immediate productivity boost from AI, while teams with weak practices get overwhelmed by the additional complexity of managing AI-generated code alongside their existing technical debt.

Parting thoughts

The teams thriving with AI assistance aren’t necessarily the most technically advanced – they’re the ones with the strongest development fundamentals. They treat AI as a powerful tool that requires the same disciplined approach as any other productivity multiplier.

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While the principles discussed here are straightforward, their effective implementation often requires a nuanced understanding of your team’s unique context. That’s where evidence-based coaching makes the difference, accelerating your journey to sustainable productivity. Let’s explore how tailored, disciplined AI integration strategies can transform your development workflow. Reach out today, and let’s build the safety nets that will make your AI-assisted development both fast and reliable.