How does constrained / structured decoding force an LLM to emit valid JSON or grammar?
Prompting for JSON works until it doesn't, and at scale the tail breaks your parser. Constrained decoding makes malformed output literally impossible to sample. The signal is knowing exactly where in the loop the constraint takes hold. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Prompting for JSON works until it doesn't, and at scale the tail breaks your parser. Constrained decoding makes malformed output literally impossible to sample. The signal is knowing exactly where in the loop the constraint takes hold. Here is the 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.
