01Module 09: LLM System DesignProduction architecture for AI-powered products - from prototype to reliable, scalable, cost-efficient systems.02LLM Product ArchitectureThe three fundamental LLM product patterns - chat, workflow automation, and autonomous agents - and how to design the production service graph for each.03Latency and Cost TradeoffsHow to decompose LLM latency and cost, choose the right optimization strategies, and define SLOs that balance quality, speed, and budget.04Context Window ManagementEngineering strategies for managing context windows in production LLM applications - history truncation, compression, RAG ordering, and prompt caching design.05Caching StrategiesFour caching layers for LLM applications - exact match, semantic similarity, provider prefix caching, and KV cache - with implementation patterns and production tradeoffs.06LLM Gateway and RoutingDesign and operate an LLM gateway - unified API, model routing, circuit breakers, budget enforcement, and fallback chains - using LiteLLM and custom routing logic.07Guardrails and Safety SystemsBuild layered defense-in-depth safety systems for LLM applications - input filtering, toxicity detection, PII redaction, prompt injection defense, output validation, and human review escalation.08Observability for LLM AppsBuild production observability for LLM applications - distributed tracing, quality metrics, cost attribution, prompt versioning, and drift detection using LangSmith, Langfuse, and Helicone.09Case Studies: Production LLM SystemsFive detailed production LLM architectures - GitHub Copilot, Notion AI, customer support bots, enterprise RAG, and code review agents - with real architecture decisions, scale numbers, and lessons learned.
