Guides/Evaluation strategy
Evaluation strategy for AI features
Shipping AI without an eval strategy means each prompt change becomes a judgment call. Strong teams run stacked loops: fast automated signals at the bottom, human judgment where stakes are high, and live experimentation once guardrails prove out.
Layered evaluation maturity
Think of layers as insurance policies — cheaper checks run continuously; expensive slices fire on cadence or sample rates. Add upper layers only when lower layers stop catching regressions you care about.
Human vs LLM judges vs automation
Human review anchors trust for novel failures; LLM judges scale rubric scoring but inherit model biases; automated metrics (exact match, embedding similarity, tool-success rates) catch drift cheaply. Production teams blend all three with clear escalation rules.
Method profiles (illustrative)
Radar shows emphasis tradeoffs — combine methods rather than picking one.
Signal strength vs cycle latency
The chart below is schematic: live experiments carry the strongest causal signal about user outcomes but take the longest to instrument safely. Parallel tracks prevent “flying blind” while experiments ramp.
Signal strength vs feedback latency (schematic)
X axis is ordinal cycle speed — not hours-to-deploy.
Starter playbook
- Freeze a golden prompt set representing top failure modes from design research.
- Pair model upgrades with diff dashboards — judge scores, toxicity hooks, tool-call success.
- Roll out behind shadow traffic before toggling user-visible defaults; promote only when offline + shadow agree.