How does LLMOps differ from traditional MLOps, and how do you version and manage prompts in production?
LLMOps is not MLOps with bigger models. What counts is the genuinely new surfaces (prompts as deployable artifacts, eval-driven development, often no training step) and treating prompts with the same version discipline as code.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
LLMOps is not MLOps with bigger models. What counts is the genuinely new surfaces (prompts as deployable artifacts, eval-driven development, often no training step) and treating prompts with the same version discipline as code.
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.