Find the Right Prompt for Every Model Your Coding Agent Runs On
Test prompt candidates, compare runtime-specific behavior, and approve the right prompt package. Stop guessing — qualify prompts against real coding-agent objectives with evidence.
Get StartedWhy Choose ProofMap
Optimize for Coding-Agent Objectives, Not Abstract Chat Quality
Define measurable objectives for your tool-using agents — code generation correctness, tool-call accuracy, structured output compliance — and optimize prompts against those targets, not vague chat preferences.
Evaluate Tool Use and Structured Outputs with Evidence
Run prompt candidates against your baseline and challenger runtimes. Inspect pass/fail evidence for tool calls, structured outputs, and agent behavior — not just text similarity.
Retrieve the Approved Prompt Package for Each Target Runtime
Once a prompt qualifies against your objective, it becomes a trusted, retrievable package. Fetch the right model-specific mapping for production, and keep fallback mappings for runtimes still in qualification.
Comparison
| Workflow need | Ad hoc prompt iteration | ProofMap |
|---|---|---|
| Testing tool-using behavior | Manually inspect agent transcripts for each runtime. No structured pass/fail criteria for tool calls. | Define objective-bound tests that validate tool selection, call structure, and output compliance across runtimes. |
| Comparing multiple runtimes | Run prompts against each model separately. Track results in spreadsheets or ad hoc notes. | Compare baseline and challenger runtimes side-by-side. See pass/fail matrices, cost differentials, and evidence per runtime. |
| Approving model-specific prompt mappings | Hope the same prompt works across models. No formal qualification or fallback strategy. | Promote qualified mappings into an approved prompt package. Maintain fallback mappings for runtimes awaiting qualification. |
| Reducing guesswork after revisions | Edit prompts and deploy. Discover regressions in production through user reports or agent failures. | Run prompt regression testing after every revision. Catch prompt drift before it reaches production, with evidence. |
Frequently Asked Questions
Is this only for coding agents?
Coding agents are the primary v1 focus because prompt drift is most concrete and measurable when agents generate code, select tools, and produce structured outputs. However, the same objective-and-runtime qualification model applies to any tool-using agent where you can define measurable success criteria.
Can I use it for other tool-using agents too?
Yes. Any agent that calls tools, produces structured outputs, or follows a runtime-dependent execution path can be evaluated against objectives and target runtimes. Coding agents are the clearest v1 ICP, but the same evidence-first approach generalizes well.
How does the product help after a model upgrade?
Model upgrades are a primary trigger for prompt drift. After an upgrade, you can run your existing prompt package against the new runtime as a challenger, compare results against your baseline, and qualify or adjust mappings before the new model reaches production.
What is the output once a prompt qualifies?
Once a prompt candidate passes your objective-bound tests on a target runtime, it can be promoted into an approved prompt package — a retrievable, model-specific mapping your agent can fetch at runtime. Unqualified runtimes receive a fallback mapping so your agent always has a safe default.
Stop Guessing. Start Qualifying.
Define your objective, test prompt candidates against target runtimes, and approve the right prompt package — backed by evidence, not intuition.
Start qualifying prompts