Feature flag prompts help teams define more than a Boolean condition. A rollout needs an evaluation context, a fallback when evaluation fails, a way to measure outcomes and a plan to remove the temporary branch. Without those decisions, flags accumulate and the application becomes harder to reason about. These prompts cover rollout design, failure testing and cleanup after the experiment or launch is complete.
Describe who evaluates the flag and why
Provide the feature, flag provider, SDK version and evaluation location. State whether a server, browser or worker chooses the variant. Include the targeting inputs and the business reason for each one. Avoid copying personal attributes into the prompt when a sanitized shape communicates the requirement. Define authorization independently of rollout visibility.
The OpenFeature evaluation-context specification describes context used in flag evaluation. It is useful vocabulary, not a guarantee that every provider handles targeting identically. Require the agent to verify your provider's semantics and defaults before proposing integration code.
Design a rollout with an observable stop condition
Ask for the eligible audience, fallback and signals that justify expanding or stopping the rollout. A percentage alone does not explain whether the same person remains in a cohort or whether background work sees the same decision. Define which consistency matters to the product. Include an owner who can interpret the evidence and take action.
Plan a feature-flag rollout for {{feature}} using {{provider_and_sdk}}.
Define evaluation location, targeting context, fallback and cohort
consistency requirements. Use {{success_and_failure_signals}} to propose
expansion and stop conditions. Describe rollback behavior for in-progress
work. Identify an owner role and cleanup criteria. Keep authorization
checks independent of the flag and mark unresolved product decisions.
Test unavailable evaluation and both branches
The fallback path should be a deliberate behavior, not an accidental default in a helper. Ask how the application behaves when the provider is slow, missing configuration or returns an unexpected type. Test the meaningful branches without trying every theoretical combination of every flag. Prioritize interactions that share data or alter a transaction.
Test {{flagged_code_paths}} using {{test_environment}}.
Cover enabled, disabled, missing flag, evaluation timeout and invalid
result where supported by our SDK. Verify the intended fallback and
independent authorization checks. Include {{interacting_flag_or_state}}
only if it changes the contract. Show how the tests avoid depending on
live production targeting rules.
Remove completed flags as a behavior change
A flag's winning branch may be clear while older clients still depend on the losing path. Ask the agent to inventory consumers, stored values and deployment assumptions. Remove the flag from code and operational configuration in a sequence that fits those consumers. Do not delete a remotely evaluated flag before understanding what a missing result means.
Plan cleanup for {{completed_flag}} with final behavior {{chosen_behavior}}.
Find all evaluations, tests, configuration and documentation consumers.
Check older deployments and background work that may still use it.
Propose removal order, focused regression checks and operational cleanup.
Keep the chosen behavior explicit and do not delete provider configuration
until the compatibility assumptions are verified.
Keep the rollout record useful
Record the decision, evidence window and any exception to the original rollout plan. Check that dashboards distinguish the relevant cohorts without exposing unnecessary identifiers. After cleanup, confirm the remaining tests describe product behavior rather than a flag that no longer exists.
Save the approved flag prompt in Kipt with variables for provider, feature and rollout signals. Pair it with the observability prompts to turn a vague launch metric into a signal someone can act on.