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