AI & Technical question
A new frontier model ships and product teams want a recommendation within days. What capabilities would you prioritize in the evals platform and operating process so model selection becomes fast, low-cost, and repeatable, for example benchmark management, judge calibration, human-review escalation, and launch criteria, while keeping the system model-agnostic and preserving clinician trust?
- Abridge
- AI & Technical
- Hard
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What this question tests
Tests the ability to design an evals platform that makes model selection fast and low cost without sacrificing clinical trust.
How to approach it
- Build a standing benchmark suite covering the main clinical workflows, ambient notes, billing, decision support, so a new model can be run immediately.
- Automate benchmark management so adding a new model to the comparison requires no manual setup, just an API key and a run trigger.
- Keep LLM judges calibrated against a rotating sample of human labeled cases so scores stay trustworthy as new models arrive.
- Define an escalation path that routes only borderline or high risk cases to human review instead of reviewing everything by hand.
- Set explicit launch criteria, for example must match or beat the incumbent model on critical error rate, before any swap goes live.
- Keep the platform model agnostic by standardizing input and output formats so switching providers does not require new eval infrastructure.
What a strong answer includes
- Prioritizes automation of benchmark runs over manual comparison, since days of turnaround is the actual constraint stated in the prompt.
- Uses a tiered review process, LLM judge first, human escalation only for disagreement or high risk cases, to keep speed and trust.
- Sets a clear bar the new model must clear before replacing the old one, protecting clinician trust even under time pressure.
Common mistakes
- Building a bespoke eval process for each new model instead of a repeatable standing system.
- Skipping human escalation entirely in the name of speed, which erodes clinician trust after one bad swap.
Likely follow-up questions
- What would you do if the new model beats the old one on average but is worse on a specific pod's workflow?
- How would you decide the sample size for human review when time is this tight?
More ai & technical questions
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More questions from Abridge
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture