AI & Technical question

A frontier code model is state-of-the-art on benchmarks, but beta users say it is inconsistently useful and sometimes suggests insecure or fabricated code. How would you design the evaluation stack and ship criteria, across offline evals, human review, and product metrics, to decide whether to launch, hold, or narrow the use case?

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What this question tests

Ability to reconcile a benchmark strong, field weak signal by building a multi layered evaluation stack and setting a defensible ship decision.

Acknowledge the benchmark field gap directly: benchmarks likely test narrow, well specified tasks while real usage involves ambiguous, multi file, or security sensitive contexts the benchmark does not cover.
Add offline evals specifically targeting the reported failure modes, insecure code patterns and fabricated or hallucinated APIs, using a curated test set built from real beta user reports.
Add structured human review on a sample of production outputs, since fabrication and subtle insecurity are hard to catch with automated evals alone, grading for correctness, security, and honesty about uncertainty.
Add product metrics from beta usage, such as user reported issue rate and code acceptance versus rejection rate, to capture real world reliability the benchmark misses.
Set ship criteria combining all three: minimum offline pass rate on the new targeted eval, human review flagging below a defined severity threshold, and beta product metrics not showing elevated complaint rates.
Decide to narrow scope, for example excluding security sensitive code generation from general availability, if failures concentrate in a specific use case rather than being uniform.

Builds a new targeted eval directly from the reported failure modes instead of relying on the existing benchmark that already proved misleading.
Combines automated eval, human review, and product metrics rather than trusting any single layer.
Proposes narrowing scope as a real option, not just launch or hold, when failures cluster in one use case.
Sets concrete, if illustrative, thresholds for each layer instead of a vague looks better now.

Trusting the existing benchmark's top ranking without building a new eval targeted at the specific reported failures.
Treating launch as binary instead of considering a narrowed scope for the riskiest use case.

How would you build the targeted eval set from beta user reports without a large existing dataset?
What would you tell users about the fabricated code risk if you shipped with the scope narrowed?