Feature Flag Strategy (PM playbook)

Feature Flag Strategy (PM playbook)

Overview

A feature flag is a runtime switch that decouples deploying code from releasing a feature. Done well, flags turn high-stakes ship dates into low-stakes config changes -- launches become measured ramps, regressions become single-toggle rollbacks, and experiments live alongside production code. Done poorly, flags become permanent technical debt: hundreds of dead toggles in code, conflicting flag states across environments, and nobody remembering what the flag controls.

This skill is the PM-facing rollout playbook. It does not describe how to wire a flag library into your codebase (that is the engineering side, e.g. your LaunchDarkly / Statsig / Optimizely / Unleash / OpenFeature install). It describes how a PM plans a phased rollout: what kind of flag this is, how it ramps, what the gate criteria are between stages, who can flip the kill-switch, when the flag retires, and how it is named so the team can find it six months later. The frameworks behind it are Martin Fowler's "Feature Toggles" taxonomy, LaunchDarkly's rollout best practices, and Optimizely / Statsig experiment playbooks.

Core Capabilities

• Flag classification -- release / experiment / ops / permission, each with its own lifespan and ownership rules.
• Rollout shape selection -- linear, segmented, geographic, A/B-with-holdout, dark launch, reverse ramp, mobile forced-upgrade.
• Kill-switch design -- pre-agreed thresholds per surface and single-config-change rollback authority for on-call.
• Holdout design -- short-term and global holdouts for long-term lift attribution.
• Flag-debt governance -- retirement dates, retirement checklist, quarterly audit, naming conventions, dependency chains.

When to Use

• Planning a launch larger than a small team can ship cold (anything customer-facing usually warrants a flag).
• Risky changes to high-traffic surfaces (search, checkout, auth, billing -- always behind a kill-switch).
• Experiments (A/B tests) with hold-out and statistical-significance gates.
• Permission rollouts (enterprise tenants, beta participants, a specific role).
• Operational levers (throttles, circuit breakers, degrade-modes).
• Migration of a deterministic feature to AI (pair with ai-feature-prd/; cost gates via engineering/llm-cost-optimizer/).

When NOT to use: one-time data migrations (use a script with --dry-run), environment configuration, permanent A/B variants that never converge (that is personalization), or flag-flagging every change (cost > value when overused).

Clarify First

Before drafting the rollout plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Flag type — release / experiment / ops / permission sets the lifespan and whether a retirement date even applies (permanent vs temporary)
• Rollout shape — linear / segmented / geo / A-B-with-holdout / dark / reverse-ramp defines the stages and gate criteria of the ramp
• Kill-switch threshold + authority — the metric that triggers rollback and who on-call may flip it; a flag without this is worse than no flag
• Retirement date — for release/experiment flags, the date the toggle is removed; omitting it is how flag debt accrues

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start