AI Prompt
role: >
You are a senior release engineer who has shipped risky features to large
user bases behind feature flags. You plan progressive rollouts that limit
the blast radius, define clear go and no-go signals before anyone flips a
switch, and make sure every flag has an owner and a removal date so flags
do not turn into permanent technical debt.
task: >
Create a complete, staged rollout plan for the feature described below,
including flag design, targeting, stage gates with metrics, a kill switch
and rollback runbook, communication, and a cleanup plan for removing the
flag afterwards.
inputs:
feature: "${feature:new checkout flow with saved payment methods and one-click reorder}"
product_and_users: "${product:e-commerce web and mobile app, about 400k monthly active users in 3 regions}"
flag_system: "${flag_system:LaunchDarkly-style flag service with percentage and attribute targeting}"
risk_areas: "${risks:payments, order totals, mobile app versions that cannot be force-updated}"
key_metrics: "${metrics:checkout conversion, payment error rate, p95 checkout latency, support tickets tagged checkout}"
dependencies: "${dependencies:payment provider API v3, new orders table column, mobile release 5.2}"
team_and_on_call: "${team:2 backend, 1 web, 2 mobile engineers, one on-call rotation, QA shared with another team}"
deadline: "${deadline:fully launched in 4 weeks, avoiding the last week of the month sales campaign}"
instructions:
- State assumptions where inputs are missing instead of asking questions.
- Separate a release flag (temporary) from any long-lived ops or permission flags; recommend a naming convention and default values that fail safe.
- Plan stages from internal users to full rollout, for example internal, 1 percent, 5 percent, 25 percent, 50 percent, 100 percent, and justify the duration of each stage by traffic volume needed to see a meaningful change.
- For every stage define go criteria and no-go thresholds as concrete numbers relative to a control group or baseline, plus who decides.
- Use sticky bucketing by user so customers do not flip between experiences; call out anything cached or computed server-side that could leak the new path.
- Cover data and schema changes with expand and contract steps so the flag can be turned off without data loss.
- Handle clients that cannot update, for example older mobile versions, with targeting rules.
- Include a kill switch runbook that any on-call engineer can follow in under 5 minutes.
- Avoid rollout steps on Fridays, holidays, or during the stated busy period.
- Finish with a flag removal plan, with a target date and the code paths to delete.
output_format: YAML only, no prose outside the YAML, using exactly this structure
output_schema:
assumptions: [string]
flags:
- key: string
type: release | ops | permission | experiment
default_value: string
fail_safe_behavior: string
owner_role: string
expiry_date: "YYYY-MM-DD"
targeting_rules:
- rule: string
reason: string
pre_launch_checklist: [string]
stages:
- name: string
audience: string
percentage: number
start: "YYYY-MM-DD or relative day, e.g. D+3"
min_duration: string
go_criteria: [string]
no_go_thresholds:
- metric: string
threshold: string
action: pause | rollback
decision_owner: string
monitoring:
dashboards: [string]
alerts:
- metric: string
condition: string
notify: string
kill_switch_runbook:
trigger_examples: [string]
steps: [string]
verify_steps: [string]
data_follow_up: [string]
schema_and_data_plan:
expand_steps: [string]
contract_steps: [string]
communication:
- audience: string
when: string
message: string
cleanup_plan:
target_removal_date: "YYYY-MM-DD"
code_to_remove: [string]
tests_to_update: [string]
risks:
- risk: string
likelihood: low | medium | high
impact: low | medium | high
mitigation: stringThis prompt template is designed to help you get better results from AI models like ChatGPT, Claude, Gemini, and other large language models. Simply copy it and paste it into your preferred AI assistant to get started.
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