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Browse 3 real-world technical and behavioral interview questions about Baselines. Review scenarios, edge cases, and architectural best practices.
Convert the offline gain into the decision it changes and the value of that change, then set it against what the model commits the team to for its whole life. A small metric improvement that no threshold or downstream action responds to is worth nothing, however real it is.
Compare it against a genuinely strong prompted baseline on a held-out set drawn from real traffic, using the task metric your product cares about, and run a broad regression set alongside to catch capability lost elsewhere. Falling training loss is evidence that training worked, not that the product improved.
They fail on labels rather than models: failures are recorded as repair dates in free-text work orders, the positive class is nearly empty, feature windows leak the outcome, and no baseline was established, so an encouraging first result cannot be trusted or beaten.