Design an evaluation pipeline for an LLM application that runs on every prompt and model change.
Eyeballing a few outputs does not scale, and a prompt tweak that fixes one case quietly breaks ten. A real LLM eval pipeline pairs a versioned dataset, layered scorers, and a CI gate. Here is the architecture.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Eyeballing a few outputs does not scale, and a prompt tweak that fixes one case quietly breaks ten. A real LLM eval pipeline pairs a versioned dataset, layered scorers, and a CI gate. Here is the architecture.
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.
