Every team using AI to write code faces the same question: how do you know when it's safe to ship?
You can't review every line with the same intensity. You'd lose the speed advantage that made AI attractive in the first place. But shipping without any review is how teams end up with $340,000 rounding errors and 3 AM production fires.
The solution is a focused review framework. Three questions that take 30 seconds to ask and can save months of problems.
We use this framework on every AI-generated output at ALL AI Agency. Here it is.
Question 1: Does It Handle the Unhappy Path?
AI is an optimist. It writes code for users who do everything right: they enter valid data, click buttons in the right order, and never lose their network connection mid-transaction.
Real users aren't like that. And real systems aren't either.
When you look at AI-generated code, the first thing to check is: what happens when things go wrong?
- What happens when the API returns a 500?
- What happens when the user submits empty data?
- What happens when the database connection drops?
- What happens when the third-party service you depend on is down?
If the code doesn't have explicit handling for these scenarios, it's not production-ready. It doesn't matter how clean the happy path looks.
The fix is usually straightforward. AI is actually good at writing error handling. It just doesn't include it by default. Ask it to add try/catch blocks, validation, and fallback behavior. Then verify the error handling actually does something useful, not just console.log(error) and move on.
Question 2: Would You Bet Your Production Database on It?
This question forces you to think about the code's relationship with your most valuable asset: your data.
AI-generated database operations often work perfectly in testing and catastrophically in production. The patterns to watch for:
Missing transactions. If the code writes to multiple tables in sequence without wrapping them in a transaction, a failure halfway through leaves your data in an inconsistent state. Order created but payment not recorded. User account updated but profile not synced. These inconsistencies are incredibly difficult to detect and fix after the fact.
No input validation before writes. The code accepts data from the user and writes it directly to the database. No checking for required fields, no verifying data types, no sanitizing input. This isn't just a bug. It's a security vulnerability.
Ignoring concurrency. What happens when two users update the same record simultaneously? AI-generated code almost never handles this. In production, concurrent writes lead to lost updates, race conditions, and data corruption that only surfaces under load.
If you wouldn't trust the code with your production database, it needs more work.
Question 3: Can the Next Developer Read It in 6 Months?
AI-generated code often has a readability problem that isn't immediately obvious: it works, it's well-formatted, but it doesn't communicate intent.
Single-letter variables in complex logic. AI loves compact code. That's great for the AI, but terrible for the human who needs to debug it at 11 PM.
200-line functions. AI doesn't have a natural sense of function boundaries. It'll put an entire feature in one function because that's what the prompt described. Breaking it into smaller, named pieces makes the code maintainable.
Zero comments explaining "why." AI adds comments that describe what the code does (// Loop through users). It rarely explains why (// Process in batches of 100 to avoid memory issues on large datasets). The "why" is what the next developer actually needs.
Code is read 10 times more often than it's written. Optimizing for readability isn't perfectionism. It's basic risk management.
Putting It Into Practice
The beauty of this framework is its speed. You're not doing a full code audit. You're asking three targeted questions that expose the most common failure modes in AI-generated code.
For a typical feature, this review takes 10-15 minutes. Compare that to the cost of a production incident, and the math is obvious.
Here's how we recommend integrating it:
- Before every merge, the reviewer asks all three questions
- If any answer is "no," the code goes back for revision, and AI is usually perfectly capable of fixing the gaps once you point them out
- Document the answers in the pull request description for future reference
Over time, this becomes second nature. Your team develops an instinct for what AI gets right and where it consistently needs guidance.
The Bigger Picture
These three questions are a starting point, not a complete review process. For complex systems, you also need to think about performance testing, security audits, and architectural alignment.
But for teams that are currently shipping AI-generated code with zero review? This framework alone will prevent the majority of production issues. It's the 20% effort that catches 80% of the problems.
Three questions. Thirty seconds. That's the difference between shipping fast and shipping right.