Turn Real Feedback Into AI Test Cases

The best AI tests often come from real failures. ProofMap helps teams turn messy feedback into repeatable evaluation scenarios.

Why Choose ProofMap

EVAL

Turn the test case creation into objective evaluations that cover prompts, runtimes, MCP tools, and fallback behavior.

DEV

Give developers concrete failure evidence and reusable tests instead of manual transcript review.

SHIP

Create more complete AI regression coverage before the workflow reaches more users or more systems.

Need	Manual approach	ProofMap
Capture behavior	Behavior is inferred from logs, demos, and scattered reports.	Behavior is measured against objective criteria and stored with evidence.
Debug failures	Developers chase failures across prompts, tools, data, and providers.	Failures point to criteria, evidence, and candidate fixes.
Approve changes	Changes ship when they look plausible enough.	Only qualified prompt packages and runtime mappings are promoted.
Maintain confidence	Confidence decays as products and models change.	Evaluation suites evolve with incidents, feedback, and platform changes.

Why use ProofMap here?

Because the test case creation needs repeatable evidence before teams can trust AI behavior at scale.

How does this save time?

Teams reuse evaluation workflows, approved mappings, and failure evidence instead of manually revalidating every change.

Does this work with MCP?

Yes. MCP tool behavior, permissions, schemas, and error paths can be evaluated alongside prompts and models.

What is the practical result?

Teams get more complete AI regression coverage and a clearer path from experiment to production.

Use ProofMap to qualify the workflow before it becomes operational risk.