AI Coding Tools Are Everywhere. Most Teams Are Using Them Wrong.

Every developer on my team uses AI coding tools. GitHub Copilot. Claude. ChatGPT. Cursor. The tools have become standard.

What hasn’t become standard is how to use them well.

I’ve watched teams get 10x productivity gains. I’ve watched teams introduce bugs faster than they can fix them. The difference isn’t the tool. It’s the process around the tool.

Here are the rules we’ve developed at WisdmLabs after a year of running AI-assisted development at scale.

Rule 1: Human Reviews Everything

This is the only rule that’s non-negotiable.

Never let AI-generated code reach production without human review. Senior developer approval on every pull request. Period.

AI code looks right. It often compiles. It sometimes passes tests. It frequently has subtle bugs that won’t surface until production.

We found a pattern in AI-generated WordPress code that would work perfectly in testing and fail silently when caching was enabled. The bug was in 12 different files before we caught it. A senior developer would have spotted the pattern on the first review.

The speed you gain from skipping review, you lose tenfold in debugging.

Rule 2: Give AI Tiny Tasks

“Build a membership plugin” → AI fails.

“Write a function to validate email format” → AI succeeds.

The difference is scope. AI handles small, well-defined tasks well. It handles large, ambiguous tasks poorly.

We’ve settled on this workflow:

One small task = One commit = One review

If the task takes more than 30 minutes to describe, it’s too big. Break it down.

I’ve seen developers spend hours trying to get AI to build entire features. They’d have finished faster writing the code themselves. AI is a force multiplier for small tasks. It’s a time sink for large ones.

Rule 3: Write Better Prompts

This is where context engineering matters.

Bad prompt: “Create a login system.”

Good prompt: “Write a WordPress authentication function using wp_authenticate(). Limit to 5 failed attempts per hour per IP. Log all failures to a custom table called wp_auth_failures with columns: ip_address, username_attempted, timestamp, failure_reason.”

The difference is specificity. The bad prompt gives AI room to make choices. AI makes bad choices. The good prompt constrains AI to implement exactly what you need.

I tell my developers: if you can’t write a specific prompt, you don’t understand the problem well enough yet. The prompt forces clarity.

Specific prompts also make review easier. You can check the output against the requirements. Vague prompts produce outputs you can’t evaluate until they break.

Rule 4: Write Tests First

Before asking AI to write code, write the tests.

Then ask AI: “What edge cases am I missing?”

AI is terrible at edge cases. It optimizes for the happy path. Tests force thoroughness.

We changed our workflow six months ago. Tests come first. AI implements to pass the tests. The bug rate dropped measurably.

There’s a bonus: AI is actually good at writing tests once you give it the function signature and describe the expected behavior. Use AI to expand your test coverage, then use AI to write the implementation. The tests keep the implementation honest.

Rule 5: Weekly AI Code Reviews

Every Friday, we spend two hours reviewing AI-generated code from the week.

We’re not reviewing for bugs—that happens in normal PR review. We’re reviewing for patterns.

What we look for:

Unnecessary packages. AI loves adding dependencies. We’ve caught imports for entire libraries to use one function. We’ve caught npm packages that duplicate functionality we already have.

Repeated patterns. When AI writes similar code multiple times, it often uses slightly different approaches each time. Consistency matters for maintenance. We refactor these into shared utilities.

Code that “works but feels off.” This is hard to describe, but experienced developers recognize it. The code does what it’s supposed to, but the approach is strange. These moments often reveal deeper architectural issues.

The weekly review also helps us improve our prompts. We see what AI gets wrong consistently and adjust how we ask.

Rule 6: Measure Everything

Track three metrics:

Time saved. How long would this task have taken without AI? How long did it take with AI? Be honest. Include the time spent reviewing and fixing.

Bug rates. Compare bugs found in AI-generated code versus manually written code. Categorize by severity. We found AI code has more minor bugs but fewer major ones. That pattern informed how we allocate review time.

Rewrite frequency. How often does AI code get rewritten within 3 months? High rewrite rates mean the code was wrong in ways review didn’t catch. This metric reveals blind spots.

Data beats assumptions. We thought AI would be best for boilerplate. The data showed it was best for string manipulation and validation logic. We adjusted our usage accordingly.

Rule 7: Rethink Estimates

AI changes how long things take. But not uniformly.

AI accelerates CRUD operations, boilerplate code, standard patterns, data transformations.

AI slows down (or adds risk to) complex architectural work, integration with poorly documented APIs, anything that requires deep context about your specific codebase.

We now estimate each task separately. “How long with AI?” and “How long without AI?” are different questions. Some tasks we deliberately do without AI because the context-setting overhead isn’t worth it.

Sprint planning got more nuanced. That’s a good thing.

The Bottom Line

AI is a fast-typing junior developer with questionable judgment.

You wouldn’t let a new hire push to production without review. You wouldn’t give a new hire your most complex architectural decisions. You wouldn’t assume a new hire understands your codebase.

Same rules apply to AI.

The teams getting the most value from AI coding tools are the ones treating AI as a tool, not a teammate. They’ve built processes around AI’s strengths and guardrails around AI’s weaknesses.

The productivity gains are real. The risks are real too. Process is the difference.

Which rule does your team struggle with most? I’ve found Rule 2 (tiny tasks) is hardest for experienced developers to accept. They want to give AI more responsibility than it can handle.