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
- A golden set — real inputs paired with agreed-good outputs, versioned next to the code. This is the source of truth.
- A judge — an evaluation model (or deterministic check) that scores each output for faithfulness, relevance, and safety.
- Safety probes — adversarial inputs that try to break the feature the way a real user or attacker would.
- 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.
© 2026 Jason Teixeira · Sage Ideas LLC · Documentation home