How do you actually implement input and output guardrails for an LLM application?
'Add guardrails' says nothing concrete. The strong answer names the specific input and output checks, the mechanisms that enforce them, the fail-safe behavior when one trips, and the honest admission that they are imperfect. Here is the implementation answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
'Add guardrails' says nothing concrete. The strong answer names the specific input and output checks, the mechanisms that enforce them, the fail-safe behavior when one trips, and the honest admission that they are imperfect. Here is the implementation answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.