Reproducing a familiar answer on a new ticket number doesn’t prove useful generalization. Tests should separate evidence-sensitive behavior from template or shortcut-driven answers, and evaluation must capture when the model should change, stay the same, or say “I don’t know.”
D'Amour and colleagues identify a related, distinct problem in Underspecification Presents Challenges for Credibility in Modern Machine Learning: a training and validation process can produce different...
A useful model should preserve its recommendation when irrelevant details change, reconsider it when relevant evidence changes, and identify uncertainty when evidence is missing.
Run these as separate sessions so that one version cannot supply hidden context to another.
A useful model should preserve its recommendation when irrelevant details change, reconsider it when relevant evidence changes, and identify uncertainty when evidence is missing. Start with controlled incident variations, a reviewable scoring rubric, and release criteria that do not allow strong average performance to conceal consequential failures. Introduction An incident assistant recommends restarting a service after receiving a report of repeated connection timeouts.
For this incident scenario, I would adapt those concepts as follows and add explicit missing-evidence coverage. The assistant should shift attention toward that communication path rather than repeat the pool-exhaustion explanation. D'Amour and colleagues identify a related, distinct problem in Underspecification Presents Challenges for Credibility in Modern Machine Learning: a training and validation process can produce different models with similarly strong test performance but different deployment behavior.
What relationship did the model actually learn? It might instead have learned that tickets containing "connection timeout" frequently end with "restart completed." Both relationships could produce the same recommendation on familiar examples. Only one might remain useful when the underlying conditions change.
How model validation standards are changing for LLM-based systems: what breaks, what carries over, and how to test output quality The post The Model Validation Playbook for GenAI: Lessons from Banking appeared first on Towards Data Science .
What a production incident taught me about trusting a model to judge another model's work The post The LLM Judge That Kept Agreeing With Itself appeared first on Towards Data Science .
Let’s say, a machine learning model achieves 95% accuracy. Continue reading on Medium »
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