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Safe Rollout Plans for AI Features

Ship LLM features without surprises: launch bars, red-team gates, cost budgets, feature flags, staged ramps, shadow and canary tests, fallbacks, spend caps, rollback triggers and model upgrades, with every calculation run.

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What you'll learn

  • Write a rollout plan for an AI feature with success metrics, guardrail metrics and launch bars.
  • Gate launches on offline evaluation, red-team results and cost and latency budgets.
  • Expose features gradually with feature flags, cohorts, shadow mode, canaries and properly sized A/B tests.
  • Protect users and budgets at runtime with fallbacks, circuit breakers and spend caps.
  • Monitor live quality and trigger automatic rollback, with human review of sampled outputs.
  • Communicate with users, handle AI incidents, and re-validate when models or prompts change.

Syllabus

Why AI Rollouts Need Their Own Plan

  1. How AI Features Differ From Ordinary Features
  2. The Rollout Plan Document
  3. Success Metrics and Guardrail Metrics

Pre-Launch Gates

  1. Offline Evaluation and the Launch Bar
  2. Red-Team and Safety Gates
  3. Cost and Latency Budgets

Feature Flags and Targeting

  1. Feature Flags, Percent Rollouts and Kill Switches
  2. Targeting Cohorts
  3. Prompts and Models as Versioned Configuration

Staged Exposure

  1. A Ramp Schedule
  2. Shadow Mode
  3. Canary Comparisons
  4. A/B Tests and Sample Size

Runtime Protection

  1. Input and Output Checks
  2. Fallbacks, Timeouts and Circuit Breakers
  3. Rate Limits and Spend Caps

Monitoring and Rollback

  1. What to Monitor After Launch
  2. Automatic Rollback Triggers
  3. Human Review of Sampled Outputs

Users, Support and Incidents

  1. Disclosure and Setting Expectations
  2. Support Readiness and Feedback Loops
  3. Incident Response for AI Features

Changes After Launch, a Case Study and a Checklist

  1. Model Upgrades and Prompt Changes
  2. Case Study: AI Summaries for Support Agents
  3. An AI Rollout Checklist