Phase 8: Launch, Metrics & Scaling AI Products · 40 min · LaunchDarkly · Python
Rollout Strategies for AI Features
You wouldn't ship a payments migration to 100% of users on day one. Don't do it to a probabilistic system either.
Hiring signal: The research behind this course is explicit that AI PMs are expected to own 'monitoring A/B tests and shadow deployments' as a core duty distinct from a traditional PM's launch checklist. A candidate who can name the specific rollout mechanism matched to a feature's actual blast radius — not just 'we did a phased rollout' — demonstrates the shipping-under-uncertainty judgment that interview loops explicitly test for.
What you will learn
- Distinguish shadow mode, canary release, gradual rollout, and feature flags as rollout mechanisms with different risk/signal tradeoffs
- Match a rollout strategy to an AI feature's blast radius and reversibility
- Design a kill-switch plan specific to a generative feature, not just a generic on/off toggle
- Produce a rollout plan for a real AI feature naming the specific mechanism, stages, and rollback trigger
The Problem
A team finishes an AI-powered contract-review feature: upload a contract, the system flags risky clauses for a legal team to review. It passed every eval in the golden dataset. The PM, eager to show launch momentum, ships it to 100% of the company's enterprise customers on a Tuesday. By Thursday, the feature has flagged an unusually high number of false positives on a contract type barely represented in the eval set — international distribution agreements, which happened to make up less than 2% of the golden dataset but a much larger share of one large customer's actual contract volume. That customer's legal team, now buried in noise, stops trusting the tool entirely and requests it be disabled account-wide. A slower rollout would have surfaced this exact gap against a small slice of real traffic, with a fast, contained way to turn it off, before it reached a customer's full contract volume and burned their trust in the feature for good.
This is the core argument of this lesson: a generative feature's eval suite, however thorough, is tested against a golden dataset — a necessarily finite, imperfect sample of the space of real-world inputs. The feature's very first exposure to full-scale, unfiltered real-world input is the actual, complete test of whether the eval sample was representative enough, and that test should never happen at 100% of traffic simultaneously. Rollout strategy is the mechanism that controls how much real-world surface area a still-unproven system is exposed to before you've confirmed the eval results hold up outside the eval set.
Four Mechanisms, Four Different Signals
- Shadow mode — the model runs against real production traffic and produces real outputs, but those outputs are never shown to users; they're logged and compared against the current system's behavior (or against human judgment, sampled). This is the lowest-risk rollout stage because it produces zero user-facing blast radius while still surfacing exactly how the model performs against real, unfiltered input distribution — the gap the contract-review example's eval set missed would likely have shown up here, as an unusual disagreement rate on international distribution agreements specifically.
- Canary release — a small, fixed slice of real traffic (often 1-5%) gets the real, live feature, with tight monitoring on that slice before expanding. Unlike shadow mode, users in the canary group actually experience the feature, so this stage tests real user reactions and behavior, not just model output quality in isolation.
- Gradual rollout — expanding the live percentage over time (5% → 25% → 50% → 100%) with defined checkpoints and go/no-go criteria at each stage, rather than a single canary-to-100% jump. This is the right mechanism when a feature's risk profile is moderate and you want multiple checkpoints to catch a problem that only appears at larger scale or after longer exposure (a quality issue that surfaces after a week of usage, not in the first hour).
- Feature flags — an independent, product-level on/off switch (often per-customer, per-segment, or global) that isn't itself a rollout stage but is the mechanism that makes every other stage actually reversible fast. A canary or gradual rollout without a working feature flag is not meaningfully safer than a full launch, because "safer" depends entirely on how fast and how completely you can turn the feature off when a problem surfaces — this is the tool LaunchDarkly and similar platforms are built around.
The rollout mechanism should be chosen by blast radius and reversibility, not by team habit
A feature that only affects what a user sees in their own session (a chat response, a generated summary shown just to them) has a small blast radius and a fast, cheap reversal — mistakes are contained and correctable per-interaction. A feature that writes to a shared record, auto-triggers a downstream action (an auto-approved refund, an auto-sent email, a routing decision that affects another team's queue), or shapes a decision with real consequences has a large blast radius and a slow, expensive, or sometimes impossible reversal. The first kind of feature can often go straight to a fast canary-to-gradual rollout. The second kind of feature earns a mandatory shadow-mode stage first, specifically because the cost of learning about a systematic problem after it's already affected real actions is categorically higher than a bit of launch delay.
A PM is choosing between shadow mode and a 5% canary release as the very first rollout stage for a new AI feature that auto-sends a follow-up email to customers based on generated content. Which is the more appropriate first stage, and why?
An auto-send action is exactly the DOWNSTREAM_ACTION blast-radius category this lesson flags as needing a mandatory shadow-mode stage first: once an email is sent, it can't be unsent, so even a small 5% canary risks real customers receiving a bad auto-generated email before the team has any real-traffic signal on the model's output quality. Shadow mode lets the team observe exactly how the model would have behaved against full, unfiltered real traffic with zero chance of an actual bad email reaching anyone, which is the right way to build confidence before accepting any live exposure at all, however small.
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