[{"_1":2,"_68":-5,"_69":-5},"loaderData",{"_3":4,"_38":39,"_66":-5,"_67":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","2026-02-16T22:45:52.410Z","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","system","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"needsLocaleCookieSync",false,"routes/_store+/_readers+/email_author.$slug/route",{"_40":41,"_65":-5},"book",{"_9":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNjgwOTY","title","System Design for the LLM Era","state","published","contactAuthorMethod","email","aboutText","

Most AI engineering today is just messy glue code around an API call. That works for a prototype, but it breaks in production.

You don't need another prompt engineering guide. You need a System Design guide tailored for the non-deterministic nature of Large Language Models.

What You Will Learn

This book is a practical, no-fluff deep dive into the architecture of real applications using AI (like Cursor, Duolingo, Doordash). We cover:

Chapter 1: LLM System Design: Why Integration Requires New Patterns

Beyond the hype: Understanding Tokens, Embeddings, and the RAG lifecycle.
Why Naive RAG fails in production and how to fix it with GraphRAG.
Agentic AI: Understanding the shift from simple prompts to autonomous agents.
Operationalizing: Performance benchmarking, testing strategies, and handling failures.

Chapter 2: Core Architectural Patterns

Resilience: Circuit breakers and fallbacks for when OpenAI goes down.
Latency: Caching strategies to make LLM apps feel instant.
Cost: Token optimization techniques to slash your API bill by 40%.
Security: Injection attacks, data privacy, and Grounding strategies.

Chapter 3: Case Study: Designing an AI-Native IDE (like Cursor/Copilot)

Handling the Context Window problem with smart code indexing.
Privacy patterns for handling proprietary user code.
Deep dive: Latency vs. Accuracy trade-offs in code completion.

Chapter 4: Case Study: Adaptive Learning Platform

Architecting an offline content pipeline vs. an online serving path.
Asynchronous processing patterns for generating personalized courseware.
Database selection: When to use Vector DBs vs. Relational vs. Graph.

Chapter 5: Case Study: AI-Powered Search for E-Commerce

Moving beyond keyword search: Hybrid Search architecture.
The Product Discovery flow: Ranking and re-ranking with LLMs.
Caching strategies for high-traffic retail events.

Chapter 6: Case Study: AI Customer Support Agent

The Golden Dataset: How to build an evaluation suite that actually works.
LLM-as-a-Judge: Automating your quality assurance.
Ingestion pipelines: Keeping your knowledge base fresh in real-time.

Gumroad link

Also available on Amazon

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