UK-based independent AI efficiency projectEvidence before claims ยท 2026

Token Optimisation: Measure Useful Verified Work

DEFINITION + MEASUREMENT FRAMEWORK

Token Optimisation: Measure Useful Verified Work

A long-scroll visual companion to the article. It summarizes the articleโ€™s evidence boundary and method; it does not extend the underlying result.

At a glance

Token Optimisation: Measure Useful Verified WorkFour-step visual: TOKEN USE; TASK QUALITY; RUNTIME; VERIFICATION. Four separate dimensions are shown; there is no composite score or implied trade-off weight.DEFINITION + MEASUREMENT FRAMEWORK1TOKEN USE2TASK QUALITY3RUNTIME4VERIFICATIONEvidence labels and limits travel with the visual.
Token optimisation is a measurement-first practice: improve resource use while preserving the quality and acceptance criteria that make work useful.

Evidence in scope

  • Keep token fields distinct
  • Compare matched work
  • Retain failed attempts and review time
  • Accept only against a declared verifier

Evidence boundary: Four separate dimensions are shown; there is no composite score or implied trade-off weight.

How to use the diagram

Declare the task, denominator, token boundary, runtime clock, verifier, failure handling, and unknown fields before comparing.

What this visual explains: Token optimisation is a measurement-first practice: improve resource use while preserving the quality and acceptance criteria that make work useful.

What it does not establish

No universal savings rate or measured winner is claimed by this definition.

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