Generative AI ยท Evaluation
Failure-to-Metric: Designing GenAI Evaluation Metrics from Real Failures
Failure-to-Metric is the method I use to design custom evaluation metrics for generative AI (GenAI). It starts from a real production failure, not a benchmark, decides whether that failure deserves its own metric, and then builds the metric around the moment things went wrong.
Why start from a failure
Building a custom evaluation metric is easy now, because most evaluation platforms let you do it. The hard part is knowing which metric will serve your real business key performance indicator (KPI). A metric copied from a benchmark can pass while a real failure keeps happening in production.
The method
- Start from a real production failurePick something that actually went wrong, not a generic test set.
- Decide whether it deserves a dedicated metricConnect the failure to the KPI it is quietly damaging. If it does not touch a KPI that matters, it may not need its own metric.
- Design the metric around the moment things went wrongLook at what happened at the decision point, not only at where the answer ended up: did the system refuse when it should have, and how costly was the answer when it did not?
Two example metrics
Refusal Rate
Scores whether the system correctly declines to answer when it should.
Higher is betterHallucination Specificity Score
Scores how dangerous the answer was when the system did not refuse.
Lower is betterTogether they cover both sides of the moment things went wrong: whether the system refused, and how costly the answer was when it did not.
Why this matters in clinical workflows
In high-stakes clinical workflows a wrong answer is not only a bad user experience, it is a patient safety issue. A confident, hallucinating model in a clinical pipeline is not a prompt engineering problem. It is an architecture problem, so I pair these metrics with deterministic guardrails, confidence scoring, and monitoring for drift in production.
Where I present this
The Failure-to-Metric Method: Building Evals Your Platform Can't
MLcon and VibeKode Berlin, AI Native Week.
Don't Let Your RAG Improvise in the ICU
Why a retrieval-augmented generation (RAG) pipeline can make up a drug dosage even when the evaluation passed, how to catch it, and what trustworthy generative AI (GenAI) looks like when the stakes are real. Watch on YouTube.
Work with me
I offer Failure-to-Metric as part of trustworthy GenAI work for clinical teams: a diagnostic against your actual failure modes, custom evaluation metrics, and monitoring loops for production.
Frequently asked questions
What is Failure-to-Metric?
Failure-to-Metric is a method I use to design custom evaluation metrics for generative AI. It starts from a real production failure, not a benchmark, decides whether the failure deserves a dedicated metric, and builds the metric around the moment things went wrong.
Why not just use the metrics my evaluation platform provides?
Platform metrics are useful, and building a custom one is easy now. The harder question is which metric serves your business key performance indicator (KPI). I start from your own production failures so the metric measures something that actually hurts.
What does Refusal Rate measure?
Refusal Rate scores whether the system correctly declines to answer when it should. Higher is better.
What does the Hallucination Specificity Score measure?
The Hallucination Specificity Score scores how dangerous the answer was when the system did not refuse. Lower is better.
How do I decide which failures deserve a metric?
I connect each failure to the KPI it is damaging. If the link is clear and the cost is real, the failure earns a dedicated metric. If it is not, it may not need one.
Did your evaluation pass and the model still failed?
I can run a short diagnostic against your actual failure modes and design the metric that measures what really went wrong.
Book a Strategy Call MayaM@MalamudAI.com