Feature Flags for AI: Progressive Delivery of LLM-Powered Features
Most teams discover the hard way that rolling out a new LLM feature is nothing like rolling out a new UI button. A prompt change that looked great in offline evaluation ships to production and silently degrades quality for 30% of users — but your dashboards show HTTP 200s the whole time. By the time you notice, thousands of users have had bad experiences and you have no fast path back to the working state.
The same progressive delivery toolkit that prevents traditional software failures — feature flags, canary releases, A/B testing — applies directly to LLM-powered features. But the mechanics are different enough that copy-pasting your existing deployment playbook will get you into trouble. Non-determinism, semantic quality metrics, and the multi-layer nature of LLM changes (model, prompt, parameters, retrieval strategy) each create wrinkles that teams routinely underestimate.
