AI evals are the right tool for repeated measurement and regression tracking. Honeypot Med exists for a different moment: when you need a suspicious prompt to become a clean artifact a founder, buyer, or security reviewer can understand fast.
| Dimension | AI evals | Honeypot Med proof bundles |
|---|---|---|
| Repeated benchmarking | Core strength | Not the main job |
| Human-readable evidence page | Usually weak or custom | Built in |
| Buyer or founder readability | Often too internal | Primary goal |
| Launch-post and shareability | Rarely considered | Launch kit ships with the bundle |
| Artifact export surface | Depends on the eval stack | HTML, PDF, JSON, Markdown, social card |
This is not anti-evals. Teams still need benchmarks, regression checks, and repeatable attack suites.
If the audience is mixed or external, a clean evidence page will usually land harder than a benchmark chart.
The strongest workflow is internal eval coverage plus public-facing or buyer-facing proof artifacts when a risky prompt surfaces.
If this page makes sense, the next useful comparisons are guardrails versus launch review and generic red-team reports versus proof bundles.