advanced~8h
AI System Architecture Patterns
Capstone view of production AI systems: how context construction, guardrails, model routers/gateways, semantic caching, agent orchestration, observability, and user feedback loops fit together into one coherent, operable architecture. Grounded in Chip Huyen's AI Engineering Ch.10 framework.
3
Subtopics
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Exercises
1
Projects
5
Quiz Qs
5
Flashcards
▶📚 Prerequisites(5)
🎓 Learning objectives
- •Draw the end-to-end request path through a production AI system: ingress → guardrail → cache → model gateway → response pipeline → observability
- •Explain how context construction (retrieval, memory, tool results) feeds into the model call and what determines context quality
- •Design a model router/gateway that selects between models based on task complexity, cost, and latency targets
- •Describe semantic caching and when it reduces latency without degrading quality
- •Identify where agent orchestration adds concurrency and how to bound its blast radius in production
- •Design an observability stack covering traces, metrics, and evaluation signals for a multi-step AI system
- •Explain how to extract user feedback from conversation signals and use it to close the improvement loop
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📂 Subtopics
Related concepts
semantic cachingmodel routingcontext engineeringguardrailsLLM observabilityagent patternsRAGhuman-in-the-loopLLM-as-judgeprompt injectioncontext window managementrate limitingstreaming
Next to learn
recommendation-system-components →llm-observability →evaluation-pipeline-design →agent-patterns →