**I spent way too long debugging an AI response that looked fine at first**

While building my Interview Co-pilot, I ran into a problem with the AI-generated interview data.

The AI was returning a response, but the structure wasn't what my application expected. It was also returning unnecessary data.

Because of that, the response didn't match my MongoDB schema and the interview report couldn't be stored correctly.

I tried a bunch of things:

* Changed/improved the prompt
* Changed the code handling the response
* Tried different approaches to the AI request
* Debugged the MongoDB side
* Made way too many API requests 😅

The last part actually became a problem because I was using a free API subscription and eventually ran out of usage while debugging.

After stepping back and looking at the complete flow, I found the main culprit: the function responsible for requesting the data from the AI wasn't structured correctly. Some of the instructions and processing were happening in the wrong order.

I ended up:

* Reorganizing the function
* Improving the prompt
* Adding **Zod validation**
* Making the expected response structure explicit
* Adding error handling so an invalid AI response doesn't result in a broken interview report

The biggest lesson for me was that an AI API returning a response doesn't mean the response is safe to use.

**The AI generates the data, but your application still needs to validate the data contract.**

Curious how other developers handle structured AI responses. Do you use Zod or another validation library?

Interview Copilot