Unit Tests Are Not Enough for AI Behavior
Traditional tests are necessary, but AI workflows need behavior evaluation too. ProofMap covers the prompt, model, tool, and context layer.
Get StartedWhy Choose ProofMap
Test probabilistic behavior
Evaluate outcomes across scenarios where simple assertions are too narrow.
Capture runtime drift
Detect changes from model updates, prompt edits, retrieval shifts, and tool behavior.
Bridge product and code
Connect engineering tests with product-level success criteria.
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 are unit tests not enough for AI systems?
They usually test deterministic code paths, while AI quality depends on prompts, models, context, and tool decisions.
Does ProofMap replace unit tests?
No. It complements them by evaluating AI behavior and production readiness.
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.