Risky Changes

A filter once passed every test but disabled ~99% of the feature on live data. Tests check code correctness; research and live measurement check whether a change is useful.

When this applies

Use for changes where being wrong is expensive or customer-visible:

• Public API fields, filters, or response shaping
• Dropping, transforming, or reordering upstream data
• Billing, pricing, caps, or quotas
• Defaults, thresholds, or provider request parameters
• Unverified assumptions about external data or user behavior

If unsure whether a change qualifies, apply this process.

1. Identify assumptions

List the assumptions the change depends on. Mark which have evidence and which are still unverified.