AI changes frequently: prompts evolve, retrieval improves, models are upgraded, and tools are added. Without disciplined experimentation, teams ship changes based on vibes. That is how quality drifts and trust erodes.
An experimentation playbook helps you learn quickly while keeping risk under control.
Separate offline evaluation from online impact
Offline evaluation answers: "Is this change better on representative cases?" Online experimentation answers: "Does this change improve real user outcomes?" You need both.
- Offline first. Use repeatable datasets and rubrics to compare prompt/model variants (see evaluation rubrics and RAG evaluation).
- Online second. Validate that improvements hold under real traffic, with real context and latency constraints (see usage analytics).
Define your success metrics and your guardrails
Every experiment needs two metric sets:
- Success metrics. Task completion, time saved, conversion, or support deflection (see value metrics).
- Guardrails. Refusal spikes, tool error rates, incident signals, privacy flags, and cost blowouts (see error taxonomy and LLM FinOps).
Guardrails prevent an experiment from "winning" while increasing risk.
Choose an experiment design that fits AI
Classic A/B tests work, but AI has extra variance. Patterns that work well:
- User-level A/B. Assign a user (or tenant) to a variant for consistency of tone and behavior.
- Switchback tests. Alternate variants by time window for shared queues or contact centers.
- Canary rollouts. Start with low-risk workflows or internal users and expand (see canary rollouts).
Use feature flags so learning is reversible
Experiments should be easy to stop. Put prompts, models, retrieval features, and tool access behind flags with fast rollback triggers (see feature flags). If signals degrade, stabilise instead of iterating blindly (see change freeze playbooks).
Maintain holdouts for honesty
Keep a protected holdout dataset and a small long-term user holdout. Holdouts expose slow regressions and measurement gaming, and they help you detect drift (see drift monitoring).
The goal is simple: learn fast, but never at the expense of trust.