Module 09: LLM System Design

Getting an LLM to produce a good response in a notebook is easy. Building a product that serves 100,000 users at sub-second latency, within a cost budget, with safety guarantees, and observable behavior - that is an engineering discipline.

This module covers the production engineering of LLM-powered applications. Not prompt engineering, not model training - the systems layer that sits between your business logic and the model API.

The LLM Application Stack

Lessons in This Module

#	Lesson	Core Skill
01	LLM Product Architecture	Choosing the right product pattern; designing the full service graph
02	Latency and Cost Tradeoffs	Decomposing latency; model tiering; cost budgets
03	Context Window Management	Conversation history strategies; lost-in-the-middle; prompt caching
04	Caching Strategies	Exact cache, semantic cache, provider-level cache
05	LLM Gateway and Routing	Multi-provider routing, fallbacks, budget enforcement
06	Guardrails and Safety Systems	Defense-in-depth safety; input/output guardrails; PII
07	Observability for LLM Apps	Tracing, quality metrics, cost attribution, drift detection
08	Case Studies	GitHub Copilot, Notion AI, enterprise RAG, agentic code review

Prerequisites

• Familiarity with LLM APIs (OpenAI / Anthropic) - Module 01
• RAG fundamentals - Module 06
• Basic FastAPI or similar backend experience
• Understanding of Redis, relational databases, and REST APIs

Key Concepts Glossary

Term	Definition
Orchestration layer	The code that assembles prompts, retrieves context, dispatches tools, and parses model outputs
Context window	The maximum token count a model can process in a single request
Prompt caching	Provider feature that caches the KV state of a repeated prefix, reducing cost by ~90%
Semantic cache	Cache that retrieves stored responses for queries with similar meaning (not identical text)
LLM gateway	A reverse proxy that adds auth, rate limiting, routing, and logging to model API calls
Guardrails	Safety checks applied to inputs and outputs before/after model calls
TPOT	Time Per Output Token - the latency contribution of each generated token
Model tiering	Routing queries to cheaper models when a high-capability model is not required
Observability	Systematic collection of traces, metrics, and logs to understand system behavior
Lost-in-the-middle	Empirical finding that LLMs attend less to context placed in the middle of long prompts