Help Product Managers Evaluate AI Behavior
AI product work needs measurable behavior, not only demos. ProofMap helps PMs define success and review evidence with engineering.
Get StartedWhy Choose ProofMap
Define product objectives
Translate user outcomes into testable criteria for AI workflows.
Review candidate behavior
Compare prompt, model, and tool changes with failure evidence and pass rates.
Make launch calls
Decide what is ready, blocked, or safe for limited rollout.
Comparison
| Workflow | Without ProofMap | With ProofMap |
|---|---|---|
| Evaluate AI behavior | Teams rely on demos, logs, and manual spot checks. | Run objective-bound evaluations against prompts, models, MCP tools, and runtime mappings. |
| Handle change | Prompt, model, context, schema, memory, or vendor changes create hidden regressions. | Compare candidates to baselines and promote only qualified packages. |
| Support developers | Developers trace failures across tools, providers, data, and one-off scripts. | Failures become repeatable tests with clear evidence and recommended fixes. |
| Control production risk | Fallbacks, permissions, and degraded modes are invented when pressure hits. | Approved mappings and fallback paths are ready before launch, incidents, or migration deadlines. |
Frequently Asked Questions
Why should PMs use evaluation workflows?
AI behavior is product behavior, so PMs need a clear way to define and judge success.
Does this replace engineering review?
No. It gives PMs and engineers a shared evidence base for product decisions.
How does this save developer time?
It makes evaluation, debugging, approval, and regression testing repeatable instead of forcing developers to rebuild evidence for every AI change.
What does ProofMap produce?
ProofMap produces objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.