Your RAG system aces your eval set but fails on real user queries. How do you close the gap?
A 90% eval score alongside angry users means your eval set doesn't resemble reality. The fix is to make evaluation follow production, not the reverse. Here is how.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A 90% eval score alongside angry users means your eval set doesn't resemble reality. The fix is to make evaluation follow production, not the reverse. Here is how.
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.
