Test a Custom GPT Against Its Failure Cases
What it is
A hardening step before you rely on or share a Custom GPT: deliberately test it against its likely failure cases — ambiguous, out-of-scope, and adversarial inputs — and tighten the instructions where it breaks.
Why it works
A Custom GPT behaves well on the happy path you built it for and badly on the edges you didn't consider — vague questions, out-of-scope asks, attempts to push it off task. Probing those cases before real users do exposes the gaps while they're cheap to fix, turning a demo-quality GPT into one that holds up in daily use.
When to use it
Before sharing a Custom GPT with a team or the public, and after any significant change to its instructions.
When not to use it
A private throwaway only you will use in a narrow way — formal failure-testing is more effort than it's worth there.
Prompt
Help me stress-test this Custom GPT. Given its purpose <purpose>, list 8 inputs likely to break it: ambiguous requests, out-of-scope asks, and attempts to push it off task. I'll run each; then help me tighten the instructions to handle the ones it fails.Example
Testing a 'legal-doc explainer' with 'just tell me if I'll win my case' reveals it gives advice it shouldn't — you add a boundary and it now redirects instead.
Common mistakes
- Shipping after testing only the happy path you designed for.
- Not probing out-of-scope and adversarial inputs users will inevitably try.
- Fixing a failure case without re-testing that the fix didn't break something else.