feature flag

**Feature flags** (also called feature toggles) are a software engineering technique that allows you to **enable or disable functionality at runtime** without deploying new code. In AI systems, feature flags provide control over model versions, prompt configurations, safety settings, and experimental features. **How Feature Flags Work** - **Flag Definition**: Define a boolean or configuration flag (e.g., `use_new_model`, `enable_streaming`, `safety_level`). - **Runtime Check**: Application code checks the flag value and executes the appropriate code path. - **Remote Configuration**: Flag values are managed through a central service, allowing instant changes without redeployment. **Feature Flags in AI Applications** - **Model Switching**: Toggle between model versions (GPT-4 vs GPT-4o) without code changes. - **Prompt Variants**: A/B test different system prompts or prompt templates. - **Safety Controls**: Instantly tighten or relax content filters in response to emerging issues. - **Feature Rollout**: Gradually enable new capabilities (tool calling, image generation) to subsets of users. - **Kill Switches**: Immediately disable a misbehaving feature or model without a full deployment. - **Cost Control**: Switch to cheaper models during high-traffic periods or budget constraints. **Types of Feature Flags** - **Release Flags**: Control the rollout of new features (enable for 10% of users, then 50%, then 100%). - **Experiment Flags**: Support A/B testing and experimentation (which prompt template performs better?). - **Ops Flags**: Operational controls for managing system behavior (enable rate limiting, switch to fallback model). - **Permission Flags**: Control access to premium features based on user tier or subscription. **Feature Flag Services** - **LaunchDarkly**: Enterprise feature management platform. - **Unleash**: Open-source feature flag system. - **Flagsmith**: Open-source with both cloud and self-hosted options. - **AWS AppConfig**, **GCP Feature Flags**: Cloud-native feature flag services. **Best Practices** - **Clean Up Old Flags**: Remove flags for fully rolled-out features to avoid code complexity. - **Default Safe**: Flag defaults should always be the safe/existing behavior. - **Monitor Flag Impact**: Track metrics by flag state to measure the impact of changes. Feature flags are a **must-have for production AI systems** — they provide the control plane for managing model behavior without the risk of full deployments.