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.