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System Design for the LLM Era
Patterns and Principles for Production-Grade AI Architecture
This book is 100% completeLast updated on 2026-02-19
STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use…
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About
About the Book
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.
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About the Author
Sampriti Mitra
Sampriti Mitra is an engineering tech lead at Sumologic and an alumna of IIT BHU, with over six years of experience designing and building scalable distributed systems. She understands the engineering realities of integrating large language models (LLMs) into production-grade systems.
Her professional background includes roles at industry-leading companies like Sumologic and Razorpay. She also runs a newsletter, Architecturally Speaking on Substack, which is dedicated to breaking down system design principles.
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