Measure the product’s task
Use a specific, useful workload rather than a generic model leaderboard. The result should support an actual architecture decision.
05 / AI evaluation · PerformancePlanned project
Tracebench
Choose the configuration. Show the trade-off.
A proposed evaluation workbench comparing AI configurations on the same task set, with quality, latency, cost and regression reports.
Brief defined / Implementation aheadPlanned work. This page records the intended problem, design direction and evidence needed for a release. No working product or measured result is claimed.
A problem worth exploring.
A model or prompt change can improve one example while making the wider product slower, more expensive or less reliable. The decision needs a repeatable comparison.
My contribution
Planned personal project. No benchmark has been run or published yet.
- Version the task set, rubric and configuration.
- Compare like-for-like workloads.
- Report sample size and uncertainty.
- Publish hard examples alongside aggregate scores.
Follow the flow.
Versioned task set
Configurations
Repeatable runs
Scoring rubric
Quality / latency / cost
A proposed workflow to test. Architecture and technology choices will be revised as the implementation earns evidence.
What the design needs to prove.
Reproducibility over a single score
A report should include configuration, dataset version, date and failure examples. Planned comparisons will separate measured provider usage from estimated pricing.
The bar for a release.
- Planned: reproducible run commands and exportable results.
- Planned: a scoring rubric with a human-reviewed subset.
- Planned: regression detection and documented limitations.
Known limitations
- Concept brief only. No quality, latency, cost or hardware-performance figures are claimed.
Keep asking better questions.
Define a small, representative task set and a defensible scoring rubric.
Next project / Planned