Google SRE workbook
Primary reliability guidance on service-level objectives, error budgets, and operating changes safely.
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Production reliability and regression gates
Turn evaluation results into release gates, monitoring, rollback, and reliability practices for AI systems.
Plain-English definition
A production regression gate is a documented condition a new model, prompt, context policy, or tool version must meet before wider release. Reliability also requires observing the live system, handling partial failures, and having a rollback or fallback path when production behavior changes.
Memory trick: A gate turns evidence into a safe release decision.
Why it matters
Offline scores do not capture every production condition. Traffic mix, provider latency, context drift, rate limits, and downstream outages can change the real result. Gates connect pre-release evidence to staged rollout and make it harder for a local improvement to create a system-wide regression.
- Gates should include quality, safety, latency, cost, and availability where relevant.
- Canary traffic reveals live behavior before a full rollout.
- Rollback needs a known-good prompt, model, configuration, and owner.
Simple example
A new prompt may ship to 5% of traffic only if evaluation quality does not fall, critical safety failures remain zero, p95 latency stays within budget, and cost per accepted task does not rise beyond the agreed threshold. The team pauses rollout if any gate fails.
- The gate names thresholds before seeing the result.
- A canary limits the blast radius of an unexpected change.
- Rollback is a product operation, not an emergency improvisation.
Example figures are illustrative calculations, not current quoted market prices.
Current example
Primary reference
This source explains the method or risk boundary. It does not replace testing the actual model, data, provider, and workload conditions in production.
Source discipline: examples on this page are illustrative; measure the real workload before making a release or capacity decision.
Common mistake
A single quality score is too weak for a release gate. A system can score well while timing out, leaking data, exceeding cost limits, or failing when a provider is degraded.
Practical takeaway
What you can do with this
Create a release checklist with offline evaluation, canary metrics, safety conditions, SLO impact, rollback target, and an owner. Run it for the next prompt or model change.
- Use hard stops for critical safety failures.
- Keep thresholds tied to user and operational outcomes.
- Record the decision and evidence for future comparisons.
Decision check: if the new version increases retries or p95 latency, does the gate stop rollout before the added capacity cost reaches all users?
Compute College learning path
AI Engineering
Step 26 of 48: Production reliability and regression gates