Deployment & Rollout
Deploy and roll out AI systems: canary releases, feature flags, A/B testing, and safe rollback strategies.
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After this section you can
- Choose between shadow mode, canary, A/B-with-judge, and blue-green for a given model or prompt change, and defend the choice on detection speed, cost, and blast radius
- Define rollback triggers with concrete thresholds and time-to-rollback budgets, then walk a real incident (OpenAI's April 2025 GPT-4o sycophancy rollback) from deploy to completed rollback
- Explain why numeric triggers cannot catch behavioral regressions, and wire judge rubrics and qualitative review so that they can
- Run a prompt registry with version-tagged metrics and an audit trail you can bisect in minutes when quality regresses
Deployment & Rollout Patterns for LLM Systems
Shadow traffic, gated canaries, judge-scored A/B tests, blue-green swaps, a versioned prompt registry, and rollback triggers that fire before users do — plus the postmortem every interview answer should reference: OpenAI’s April 2025 GPT-4o sycophancy rollback.
Deploying LLM systems differs fundamentally from traditional software deployment. Model behavior is non-deterministic, prompt changes can cascade unpredictably, and quality regressions are harder to detect than functional bugs. This section covers battle-tested patterns for safely rolling out model updates, prompt changes, and new AI features to production.
Shadow Mode Deployment
Shadow mode (also called "dark launching") runs a new model or prompt version alongside the production system without exposing results to users. All requests are dual-written: the production path serves the user, while the shadow path processes the same input asynchronously for comparison.