AI made code easy. Trust is now the bottleneck.

Has this ever happened to you?  

Your pull request looked perfect. It passed every test. The review was approved. 

But it still broke in production.  

Not because the implementation was wrong, but because the test that mattered was never written.  

AI has dramatically accelerated code generation. As teams produce more code than ever, the bottleneck has shifted from writing code to reviewing and validating it. 

And reviewing AI-generated code takes more than verifying that it works. The hardest part is uncovering the edge cases, assumptions, and scenarios no one thought to test in the first place. 

The research backs this up. 

A recent study found that developers correctly judged 74% of accurate  AI-generated assertions but only 49% of incorrect ones, despite being similarly confident in both cases. In other words, developer confidence in AI-generated output doesn't always match its accuracy. 

The good news: you can use AI to review more deliberately before code reaches production. One of its biggest strengths is helping developers think more broadly about testing and risk before code reaches production. 

Here are three ways GitHub Copilot can help: 

1. Start with the tests that don’t exist 

Before requesting a human review, run Copilot Code Review against your pull request to identify missing edge cases and test gaps.

Instead of only validating the tests you wrote, Copilot helps identify the scenarios you never thought to test.

The takeaway: The best reviews begin by finding the scenarios you haven’t tested yet.  

2. Challenge the assumptions behind the code 

Missing tests often come from hidden assumptions.  

Before approving a change, ask Copilot to uncover the assumptions built into the implementation. 

Ask Copilot: 

When examining this code, what assumptions about business logic, design preferences, or user behaviors have been made?         

Or: 

What potential issues or limitations with this approach should I consider?         

These prompts help Copilot surface hidden assumptions that might otherwise slip through the cracks. 

The takeaway: Strong reviews verify the thinking behind the code, not just the code itself. 

3. Review the code like a teammate would 

Even well-tested code can become tomorrow’s technical debt if it’s difficult to understand.  

Ask Copilot: 

If another engineer inherited this code six months from now, what parts would be hardest to understand or maintain?         

Copilot can point out opportunities to improve naming, structure, and clarity before the code becomes technical debt. 

The takeaway: Code is finished when another developer can understand it, not just when it works. 

Moving forward together 

As code moves faster, trust has to move with it. 

The next opportunity is finding the risks hiding in the tests you never thought to write. 

The better your review process, the easier it becomes to turn AI-generated code into production-ready software. 

Learn more techniques for reviewing AI-generated code >

✨ This newsletter was written and produced by Gwen Davis. ✨