One thing that's been bothering me lately: benchmark performance often tells me almost nothing about whether a workflow will survive production usage.[D]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
I've seen systems score well internally and then immediately fail under:
- ambiguous user intent
- messy real-world context
- contradictory instructions
- long-running sessions
Feels like evaluation still heavily rewards clean-task optimization instead of behavioral robustness.
What are people using beyond standard eval pipelines?
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