How to Test AI-Generated Code Before Shipping to Production
The speed of AI code generation creates a trap: you produce working-looking code so fast that testing feels like it's slowing you down. But AI code is rarely production-ready on the first pass. The time you save generating, you can easily lose debugging in production. A short, disciplined test pass is how you keep the speed and skip the pain.
Here's a checklist you can run every time, roughly in order.
1. Read it before you run it
Spend two minutes understanding what the code is supposed to do. You can't debug — or trust — code whose purpose you can't state. If a section is opaque, ask the AI to explain it, then decide whether the explanation makes sense.
2. Run it in isolation
Execute the new code on its own before wiring it into everything else. Isolating it means that when something breaks, you know where. This is where a tool that lets you run and inspect your own build pays for itself.
3. Test the happy path, then attack it
Confirm it does the right thing with normal input. Then deliberately try to break it:
- Empty input, missing fields, and nulls
- Zero, negatives, and very large numbers
- The first and last item in any collection
- Malformed or unexpected data types
- Slow or failed network responses
Ask the AI itself, "what inputs would make this fail?" — then feed those in.
4. Check the error handling actually works
Force the failures you can: disconnect the network, pass a bad value, point it at a file that isn't there. Confirm the code fails clearly instead of crashing or, worse, failing silently and continuing with wrong data.
5. Verify the outputs, not just the absence of errors
"No error" is not "correct." For a handful of known inputs, confirm the exact output you expect. Silent logic errors only show up when you check results against reality.
6. Do a security pass
Look specifically for: unvalidated user input, secrets or keys hardcoded in the source, missing authentication or authorization checks, and anything that trusts data it shouldn't. If the code touches user data or the network, this step is not optional.
7. Check for outdated or deprecated approaches
Confirm the libraries and patterns are current. A model may reach for something that was standard a year ago. A quick look at recent documentation catches most of this.
8. Integrate in small steps
Bring the tested piece into the wider app incrementally and re-check at each step, rather than assembling everything and hoping it fits.
Where Vibe Coder Playground helps
Steps two through five all require the same thing: running your own build and watching it behave. Being able to test, view the live state, and edit in place turns this checklist from a chore into a fast, tight loop — which is the whole point of building with AI in the first place.