Scorecard

Scorecard Pro Tips

➩ Start with “golden paths” + “gotchas”: seed 20–50 real prompts users actually ask, plus known failure cases (jailbreaks, long contexts, ambiguous asks) for an honest baseline.
➩ Lock the metric bundle: track accuracy, tone, safety, and consistency; keep the same set across runs so trends are comparable.
➩ Change one thing at a time: A/B prompt, model, temperature, or tool policy, not all at once, then review Run History and side-by-side comparisons.
➩ Patch coverage with synthetic data: auto-generate long-tail cases, curate a few by hand, and merge into your canonical test set.
➩ Promote from playground → system: when a prompt looks good, add it as a candidate system and re-run the full suite to avoid “demo drift.”
➩ Watch regressions like a hawk: run the whole suite before shipping; if any critical metric dips, block the release and iterate.
➩ Scope evals to real scenarios: mirror production constraints (context limits, tool timeouts, guardrails) so scores reflect reality.
➩ Instrument live behavior: pair scheduled evals with continuous evaluation to catch emerging failure patterns in real time.
➩ Tie scores to business KPIs: map eval metrics to CSAT, deflection rate, or conversion so wins translate to product decisions.
➩ Make it a team ritual: invite PM, Eng, QA, and Risk; review charts weekly and document what ships only when it beats the baseline.