Best practices for using flag targeting rules in an experiment

In LaunchDarkly, flag targeting rules allow for hyper-specific targeting of specific individuals or groups, enabling hyper-targeted experimentation. Flag targeting rules evaluate users based on predefined criteria and serve variations accordingly. The order of flag rules matters, as the top rule evaluates first, followed by subsequent rules in sequence. This allows for complex targeting scenarios to be achieved through a combination of separate and combined rules. Targeting rules can also be used to capture margins by leveraging user attributes attached to the context and experiment learnings. By adding new targeting rules after running an initial experiment, users can quickly capitalize on winning margins and improve their experimentation program's efficiency. Effective use of flag targeting rules requires careful consideration of rule clarity and targeting criteria to ensure optimal results.