Production AI Systems

What You'll Learn

• Define what 'production-ready' means for an AI system and identify the gaps between a prototype and a reliable deployment
• Design a monitoring strategy that catches output quality degradation, latency regressions, and cost anomalies before they become incidents
• Implement layered error handling including retries with backoff, fallback models, and human-in-the-loop escalation paths
• Apply concrete cost optimization techniques (semantic caching, model routing, and prompt compression) to reduce spend without sacrificing quality
• Build an evaluation framework that tests AI behavior continuously, catches regressions, and gives you confidence when deploying changes