Production evals must connect model behavior to business KPIs. We blend automatic metrics (BLEU, rouge‑L, toxicity) with human‑in‑the‑loop spot checks on high‑value segments.
Our loop:
- Define task rubric and pass/fail rules
- Generate diverse eval sets with synthetic augmentation
- Score with programmatic judges and human audits
- Track cost/performance drift weekly
This keeps quality stable while you iterate prompts, tools and models.
What to evaluate
- Task success: pass/fail against rubric with reasons.
- Safety: toxicity, PII, jailbreak resilience.
- Cost & latency: per request and per business event.
- Drift: weekly comparison vs last known good version.
Evaluation architecture
- Seed a golden set from real data; augment synthetically.
- Score automatically with programmatic judges; sample for human audits.
- Create dashboards for win/loss reasons and regression diffs.
- Gate deploys with quality thresholds and canary traffic.
Tips
- Prefer model‑agnostic rubrics; avoid overfitting to phrasing.
- Measure business KPIs (demos booked, CSAT) not just BLEU.
- Keep prompts versioned and tracked in traces.
Case study: reducing cost without hurting quality
By switching to a smaller model on non‑critical intents and enforcing guardrails, one team cut compute by 42% while keeping task‑success within ±1.5%. Canary evals and human audits caught regressions early.