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Understanding Modern LLM Systems
A Field Guide to RAG, Agents, and Beyond
For PMs, presales, and managers who work with AI but don’t write code: how RAG, agents, fine-tuning, compliance, and security actually fit together.
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About
About the Book
Featured on the Leanpub home page, October 2026.
Three things most people in AI meetings get backwards:
- "They don't train on our data, so we're fine." Not training on your data and not keeping it are two different promises. OpenAI's API hasn't trained on customer data by default since March 2023, but abuse-monitoring logs can still hold prompts and responses for up to 30 days.
- "Embeddings are just numbers. They're anonymous." In a 2023 EMNLP study, 92% of 32-token inputs were recovered word for word from one embedding model, including 89% of patient full names in a clinical test.
- "An attacker would need access to our systems." One ordinary-looking email was enough to make Microsoft 365 Copilot leak data (EchoLeak, CVE-2025-32711, rated 9.3 out of 10).
This book explains why, and how the whole system fits together.
Who it's for
PMs, pre-sales, founders and managers who work alongside AI projects but don't write code. You can follow a conversation about RAG, embeddings and agents well enough to nod along. Drawing the system on a whiteboard, or saying which part is broken when the answers come back wrong, is another matter. This 67-page field guide closes that gap.
If you work on LLM training or inference optimization, it will read as introductory.
What's inside
One question, followed end to end through a complete LLM system. The throughline is a working analogy: a secretariat preparing a briefing for an executive.
- Before We Begin: What Problem Is This Whole System Actually Solving?
- Chapter One · RAG Core Flow: How a Question Becomes a Cited Answer
- Chapter Two · Model Adaptation: Prompt, RAG or Fine-Tune?
- Chapter Three · Four Core Technical Mechanisms
- Chapter Four · Agents: When the Model Starts Deciding and Acting
- Chapter Five · Compliance: Where Does Confidential Data Actually Go?
- Chapter Six · Security: When Someone Means to Cause Harm
- Chapter Seven · Beyond RAG: The Full Landscape
- Appendix · Toolkit Overview, plus a one-page Quick Reference
No math, no code. Seven original diagrams. 49 footnotes to primary sources: papers, CVE records, OWASP, NCSC, and official vendor documentation. No blog-post summaries.
Not sure yet? Read the free sample first. 👇
Why pay when it's free online?
The full text is free at llmknowledge.org, and it will stay that way. What you get here is the typeset edition: PDF for reading straight through or printing, EPUB for Kindle (via Send to Kindle), Apple Books or Kobo. Offline, annotatable, yours, with free updates. It also keeps the online version free for everyone else.
Questions
Does the EPUB work on Kindle? Yes. Upload it at amazon.com/sendtokindle.
Do I get updates? Yes, free. New versions appear in your Leanpub library.
Is there a Chinese edition? Yes, sold separately on Gumroad.
Refunds? 60 days, full refund, handled by Leanpub.
Author
About the Author
koe
I don’t write code, and I came to AI the way many readers of this book will: needing to understand systems that other people build. I learned the fundamentals first, then RAG, then everything that kept turning up around it, and along the way I kept explaining it all to non-engineers.
My working rule is to trust nothing secondhand. Before a claim goes on the page, I trace it back to the paper, the CVE record, or the official documentation. I write in English and Chinese, and I keep both versions of everything in step.
You can reach me at koe@llmknowledge.org.
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