01Module 08: AI Product EngineeringDesign, build, and ship AI-powered products that users trust - streaming UX, latency management, error handling, rollout strategies, personalization, and quality measurement.02AI Product Design PrinciplesPrinciples for designing AI products that build trust, degrade gracefully, and solve the last-mile problem between model capability and user value.03Streaming UX for LLMsServer-sent events, streaming tokens, TTFT optimization, and building responsive AI chat interfaces that feel instant even under production load.04Handling LLM LatencyPerceived latency, progressive rendering, streaming, prompt caching, and UX patterns for making slow AI responses feel fast.05AI Error Handling and FallbacksGraceful degradation, retry logic, circuit breakers, fallback model chains, and user-facing error messages for production AI systems.06Prompt UX PatternsPrompt scaffolding, slash commands, context transparency, and mode switching in production AI interfaces.07AI Feature Flags and RolloutsSafely rolling out AI features with canary deployments, quality-gated rollouts, A/B testing, and kill switches.08Personalisation and MemoryUser preference learning, conversation memory architecture, and personalised AI experiences that persist across sessions.09Measuring AI Product QualityBuild a production-grade quality measurement system for AI products using explicit feedback, implicit behavioral signals, LLM-as-judge, and composite scoring.
