How do you evaluate generative output quality (text and images) when there's no single correct answer?
For open-ended generation there's no ground-truth string to match, so accuracy is meaningless. The field relies on a layered mix of automatic, model-based, and human metrics. Here is how to assemble a credible eval.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
For open-ended generation there's no ground-truth string to match, so accuracy is meaningless. The field relies on a layered mix of automatic, model-based, and human metrics. Here is how to assemble a credible eval.
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.
