Launch in stages, with a kill switch

Lesson objective

By the end of this lesson, you will be able to write the launch plan for an AI feature in stages (shadow, internal, canary, percentages), with quantified promotion criteria, an observation period and a kill switch tested before the first user.

Where it fits

Launch without betting everything

How do you expose an AI feature to real users in stages, while being able to reconstruct every response?

Lessons in this module

1. Launch in stages, with a kill switch (this lesson)
2. Log and trace without exposing data

What you will learn in the course

This lesson is part of the course Ship and monitor an AI feature in production

• Design the staged launch of an AI feature (feature flag, shadow, canary, percentages) with quantified promotion criteria and a tested kill switch.
• Define what you log and trace for each model call, what you mask or exclude (personal data), how long you keep it, and with which tool.
• Set up quality signals in production (user feedback, sampled human review, online evaluations, drift) and connect them to the evaluation set.
• Manage cost per request and per useful outcome, and perceived latency (budgets, alerts, caching, streaming, response length, model choice).
• Diagnose AI-specific incidents (drift, provider outage, deprecation, call loop, cost spike) and plan the right fallbacks.
• Run a model or prompt change with regression evaluations, shadow comparison, staged rollout and a rollback plan.
• Build the quality, cost and latency dashboard of an AI feature and report to stakeholders with decisions to make.

Related courses

• Build an AI assistant for your productAdvanced · ~3 hr
• Design a RAG architecture that fits your productAdvanced · ~3 hr 30 min
• Evaluate an AI feature: test sets, metrics and LLM judgesAdvanced · ~3 hr