The AI Engineering Guidebook
Our 4th FREE book is here (350+ pages)!
Today, we are happy to announce our much-awaited AI Engineering Guidebook (2025 Edition):
You can access it on Google Drive here for free →
This guidebook focuses on the engineering layer behind LLMs, covering how modern AI systems are actually designed, built, and deployed.
The book covers:
How LLMs are built, trained, and generate text
Prompt engineering and why context matters more than prompts
RAG systems, architectures, and retrieval tradeoffs
Fine-tuning techniques, including LoRA and reinforcement learning
Context engineering workflows for agents and LLM apps
AI agents, design patterns, memory, and multi-agent systems
Model Context Protocol (MCP) and how it differs from function calling
LLM optimization techniques like KV caching and model compression
Evaluation, observability, and deployment of LLM systems
Each chapter focuses on engineering decisions, tradeoffs, and real-world system design rather than surface-level usage.
We will continue to update this guidebook as we publish more in-depth analyses on AI engineering, agents, MCPs, and LLM systems.
You can access the book on Google Drive here for free →
As always, we would love your feedback. If you have suggestions, topics you want us to cover next, or sections that need improvement, just reply to this email.
More hands-on content around Agents, RAG, and production LLM systems will keep coming your way, as always.
Happy learning!