FREE 367 Pages Premium Guide for LLM System Design + source code

Most resources on large language models (LLMs) are either research papers, which expect you to know how to train advanced models, or quickstart guides, which stop after the first API call. But the important work happens in the middle stage, where people build systems that are efficient, affordable, reliable, safe, and able to grow. This middle stage is also important in system design interviews.

Source Code: https://github.com/lamhotsiagian/llm-system-design

Modules:

Preview: https://drive.google.com/file/d/10BprurqQg2WjD4CvVOUBRNgWLJ602g-l/view?usp=sharing

Premium Guide: https://shop.beacons.ai/aiengineeringinsider/454e1828-17a8-4b3f-83ba-9fe286e5d942

This book focuses on this middle stage by offering 50 chapters divided into 13 parts, all based on a companion project you can run offline on your own computer without needing API keys.

You learn about capacity and cost calculations, training models across multiple machines, improving how models make predictions, RAG from breaking data into parts through ranking and evaluation, agent loops and using tools, safety checks and testing, evaluation setups and A/B testing, automatic scaling and reliability, and then eight full case studies that show how to build real products. The book ends with cost management and machine learning operations.

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