AI System Design

A working prompt is a demo. A system is what survives real traffic, real users, and a real budget. AI system design is the discipline of wrapping an unreliable, non-deterministic, metered model in enough engineering that the result is dependable.

The reframe

Treat the LLM as one unreliable component in a larger system — like a flaky third-party API you don’t control. Your architecture’s job is to make a trustworthy product out of an untrustworthy part.

In this section

Design Principles The mindset shift — designing for non-determinism, failure, and verification rather than against them.

LLM Application Architecture The anatomy of a real LLM app: gateway, orchestration, retrieval, tools, guardrails, and observability.

Cost, Latency & Reliability The three-way trade-off — modeling token cost, caching, model routing, streaming, and fallbacks.

What you’ll be able to do

Sketch a production-grade architecture for an LLM feature, identify where it will fail, and make deliberate trade-offs between cost, speed, and reliability instead of discovering them in an incident.

Prerequisites

LLM Engineering. The RAG and AI Agents sections build directly on these principles.