How do you design stopping criteria and stop sequences for an LLM in production?
Generation has to end somewhere, and the wrong stop rule either truncates answers or wastes tokens on trailing garbage. Here is how the EOS token, stop strings, and max-token caps actually interact.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Generation has to end somewhere, and the wrong stop rule either truncates answers or wastes tokens on trailing garbage. Here is how the EOS token, stop strings, and max-token caps actually interact.
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.
