How to use feature flags with AI systems
Feature flags are a standard tool for gradual software rollouts, but AI systems introduce dimensions that standard feature flag patterns do not handle well. Prompts, models, and inference configurations need their own flagging approaches.
How to handle rate limits in production AI systems
Rate limits are the constraint that most AI applications eventually run into. Building systems that handle them gracefully, rather than breaking when they appear, is a core production engineering concern.
Streaming AI responses: what changes in your architecture
Streaming AI responses (receiving output token by token rather than waiting for the complete response) changes the perceived performance of AI features dramatically. It also introduces architectural challenges that do not exist in standard request-response systems.
How to manage AI model upgrades without breaking production
Model providers update their underlying models regularly, sometimes without announcement and without changing the API version. The same endpoint that returned reliable outputs last month may behave differently today. Managing this risk requires different practices than managing software library upgrades.
How to build fallback chains in AI systems
AI systems fail in ways that traditional software does not. Model APIs go down, outputs fail validation, latency spikes, and costs spike. Fallback chains are the engineering pattern that makes AI-powered features resilient to these failure modes without requiring constant human intervention.