Lion Cipher Lab
Stop shipping LLM regressions: build a golden-set eval that fails your build when quality drops
- AI Security
- Advanced
- ~60 min
- Free · no account required
Why it matters
Your LLM feature — a classifier, an extractor, a summarizer — works today. Tomorrow you tweak the prompt or bump the model version and quietly break 8% of cases. Nobody notices until a user does. Manual 'looks fine to me' spot-checks miss exactly these small, silent regressions because a human eyeballs three examples, not eighty. A regression gate is the same discipline software teams already trust for code: freeze a known-good baseline, run it automatically on every change, and fail the build when quality drops. It turns 'we think the AI still works' into a red or green check on the pull…
What you'll learn
- freezing a version-controlled golden set (labeled input -> expected output)
- scoring an LLM feature with exact-match accuracy vs an LLM-as-judge rubric
- handling nondeterminism (temperature=0, k-runs, threshold not equality)
- establishing and storing a baseline score
- writing a regression gate that exits non-zero when quality drops past a tolerance
- wiring the gate into GitHub Actions so a regression blocks merge
- picking a tolerance that catches real drops without failing on noise
- judging when to gate (anything users touch) and when not to (early exploration)
Steps in this lab
- Why a gate beats 'looks fine to me'
- Set up a free local model so the whole gate costs nothing
- Freeze a golden set — and version-control it
- Write the scorer — exact-match for a classifier, and when you'd need a judge instead
- Establish the baseline and store it
- Write the gate: exit non-zero when the score drops past tolerance
- Prove it fails: break the feature and watch the gate go red
- Wire it into CI so a regression blocks merge
- The judgment: tolerance, when NOT to gate, and ratcheting the baseline up