← ⚙️ System Design for AI in Production
Core
Observability for AI Systems
You cannot operate what you cannot see, and an AI system has failure modes a normal service does not: the prompt, the retrieved context, the model output, and the slow drift in quality over time. Observability for AI means logging and tracing every stage of the chain with a shared request ID, so when an answer is wrong you can reconstruct exactly why.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
LESSONS THAT TEACH THIS
Foundations of Forward Deployed EngineeringBuild an evidence map of an unfamiliar system28 min→Foundations of Forward Deployed EngineeringLeave tests that catch regressions and explain their limits30 min→Foundations of Forward Deployed EngineeringPut the prototype in users’ hands with a bounded access plan28 min→
PRACTICE THIS IN REAL QUESTIONS
System Design & Production EngineeringWhat do p50, p95, and p99 latency actually mean, and why do production engineers obsess over the tail?→System Design & Production EngineeringIt's 9am Monday and p99 latency is 10x normal. Walk me through your first 30 minutes.→ML System Design (Product)Build a fraud-detection model for payments.→System Design & Production EngineeringDesign observability for an LLM application. What do you log, trace, and alert on, and how is it different from normal services?→MLOps & ML EngineeringDesign an automated retraining pipeline. What should trigger retraining?→MLOps & ML EngineeringWhat is training-serving skew, and how do you keep online and offline features consistent?→