JEV / DECISION ENGINEERING
Jev vs an LLM for classification: compare fairly
Compare quality, end-to-end latency, and total workflow cost on the same task instead of repeating a headline speedup.
Freeze the task and examples
Use the same real, de-identified inputs and human-labeled outcomes. Include both easy and ambiguous cases. Do not select only a binary task favorable to one approach.
Compare working pipelines
Give the LLM sensible structured output and validation. Include state construction, thresholds, review, and subsequent actions for Jev. Count network and preprocessing time on both sides.
Publish the full result
Report accuracy, costly errors, p50/p95, concurrency, input length, total cost per thousand items, and review rate. The biggest speed and cost multipliers in TypeSafe's launch material came from specific evaluations.
A minimum benchmark report
Publish sample origin and count, labeling rules, model versions, region, concurrency, p50/p95, cost per thousand items, failure cases, and the human-review rate. Without your own API run, cite only scoped official or author-reported data.