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The eval method

The eval method

A short philosophy, then four deep-dive guides. The whole method reduces to one idea: replace opinions about AI quality with computed scores from real commands.

#The four moving parts

  1. A golden set — real inputs paired with agreed-good outputs, versioned next to the code. This is the source of truth.
  2. A judge — an evaluation model (or deterministic check) that scores each output for faithfulness, relevance, and safety.
  3. Safety probes — adversarial inputs that try to break the feature the way a real user or attacker would.
  4. A CI gate — the suite runs on every change; a score below the ratcheted floor blocks the merge.

#The principles

  • Computed, not claimed. A score comes from a command anyone can re-run — never from an opinion.
  • Ratchet, don’t just pass. The floor only goes up, so quality can’t silently erode over time.
  • Earn down human review. Start with a human in the loop; remove approval points only when evals prove it’s safe.
  • No fake green. If the gate should be red, it goes red — publicly. (This site’s own scorecard works the same way.)
Deep dives, each with an animated diagram: The CI eval gate · Safety probes · Golden sets & judges · Human approval.
See it run
The live eval grades an AI in real time — press run and watch it fail probes.
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