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WorkModel Sensitivity Evaluation Suite
PythonLLM EvaluationAnthropic APIPrompt Engineering
Built at JWX to answer a concrete question: how much does prompt spec-detail actually matter once you factor in which model is running it? The harness cross-tests spec-detail level against model choice across Sonnet 5, Opus 5, and Fable 5, running ~90 evaluation runs spanning 3 models and 5 real engineering tickets.
Each run is scored on output quality against the ticket's actual requirements, not just pass/fail, so the results show which spec-detail level pays off per model rather than assuming one prompting style generalizes across providers. A live UI dashboard surfaces per-job cost and alarms as a supporting view into the run data.
Key Highlights
- ▸~90 evaluation runs cross-testing spec-detail level against model choice
- ▸3 models (Sonnet 5, Opus 5, Fable 5) evaluated against 5 real tickets
- ▸Quality-scored per run, not just pass/fail
- ▸Live dashboard for per-job cost and alarms as a supporting view