L170 · Under the hood · 2 min

Guardrails on the way out

Output guardrails check the model's answer before you see it, catching leaks, unsafe advice and claims the app cannot back up.

01 · The idea

Even with careful instructions, a model can say something it should not: reveal hidden instructions, repeat private data from a document, give medical dosages, or promise a refund the company never offered.

An output guardrail reads the draft answer first. It might scan for personal data, check the tone, compare claims against the sources the app retrieved, or use a second model as a reviewer. Some checks are strict rules, such as never showing an account number in full.

If the answer fails, the app can rewrite it, swap in a safe fallback like "I can't help with that here, a staff member will contact you", add a warning, or route the case to a person.

Output checks matter most where answers have consequences, like health, money and legal topics. They add a little delay, so teams put the heaviest checks on the riskiest topics and lighter ones elsewhere.

02 · Think of it like this

It is like an editor reading a reporter's story before it goes to print, checking names, facts and anything that could get the paper sued.

03 · Do it now

04 · Quick check

A shop's bot drafts "Yes, we will refund you double." There is no such policy. What should an output guardrail do?