The difference between using AI and understanding it.

A fundamentals-first guide for engineering leaders who want clarity before committing to bets.

AI is entering production faster than engineers can understand it. It doesn’t fail like software. There’s no debugger, no obvious point of failure. You’re shipping blind. This book gives you the mental models to see inside the black box and make real decisions about LLMs.

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By Raahul Seshadri

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What You’ll Be Able to Do After Reading This Book

You won’t just “know more about AI.” You’ll be able to reason about it.

Decide when an LLM is the right tool

Not every problem should be solved with AI. You’ll gain the intuition to tell the difference before you build the wrong thing.

Predict how a model is likely to behave in new situations

Instead of trial and error, you’ll understand why certain prompts work, why others fail, and why outputs vary.

Understand where hallucinations actually come from

Not just that they happen, but why they happen, and which kinds of problems make them more likely.

Make sense of “reasoning” without mysticism

You’ll see how LLMs can appear to reason, what that means mechanically, and where that illusion breaks down.

Design AI features instead of poking at them

You’ll be able to think in terms of inputs, constraints, failure modes, and tradeoffs. The same way you do with any other system.

Explain what’s happening to other engineers

Without hand-waving. Without hype. Without pretending it’s magic.

Table of Contents

A chapter-by-chapter breakdown of what you'll learn.

1

Cutting Through the AI Jargon

What words like intelligence, learning, and reasoning actually mean when applied to machines, and why most debates about AI start with bad definitions.

2

A Crash Course on How LLMs Work

The core principle behind every language model, explained simply: what the model is really optimizing for and how that turns into useful behavior.

3

The Word is a Lie

What LLMs actually operate on (tokens and numbers), how they represent relationships between words, and why meaning is something we add—not the model.

4

How LLMs Are Trained

How massive amounts of text turn into behavior, why training takes so much compute, and what the model really learns from data.

5

Why LLMs Don’t Give the Same Answer Twice

Where randomness comes from, how decoding works, and why variability is a feature and not a bug.

6

The Two Sides: Creativity & Hallucination

Why the same mechanism that makes models creative also makes them wrong, and how to reason about that tradeoff.

7

How Reasoning Works in LLMs

What’s actually happening when a model solves a problem, what it can and can’t do reliably, and where the illusion of reasoning breaks down.

How AI Thinks Book Cover

Who This Book is For (And Isn't)

This book is for technical and product leaders who need clear mental models of AI, instead of hype, buzzwords, or cargo-cult explanations.

CTOs & VPs of Engineering

Senior leaders responsible for technical direction who need to reason clearly about what AI is and is not before making bets.

  • Cut through AI hype and vague claims when evaluating strategy
  • Understand what LLMs actually optimize for, and what they never will
  • Make informed decisions grounded in reality, not vendor narratives
  • Push back on misleading discussions about “intelligence” and “reasoning”

Engineering Managers

Leaders managing teams building with or around LLMs who need accurate intuition to guide decisions and expectations.

  • Develop a correct mental model of how LLMs behave
  • Explain variability, hallucinations, and failure modes to stakeholders
  • Set realistic expectations for “reasoning” and problem-solving
  • Lead teams without over- or under-trusting the model

Tech Leads & Architects

Senior engineers responsible for designing systems that incorporate LLMs.

  • Understand what models actually operate on (tokens, probabilities, representations)
  • Reason about randomness, decoding, and nondeterminism
  • Design systems that account for hallucination and uncertainty
  • Avoid architectural decisions based on false assumptions about “understanding”

Startup Founders

Builders using LLMs as core technology who need clarity before scaling ideas, teams, or narratives.

  • Separate real capability from demo magic
  • Avoid building products on misunderstood assumptions
  • Communicate honestly with investors, customers, and teams
  • Identify where LLMs add leverage, and where they don’t

Data Science & ML Leaders

Managers of ML or data teams who want a deeper explanation of model behavior beyond surface-level explanations.

  • Understand what training actually teaches a model
  • Reason about behavior emergence from large-scale data
  • Explain creativity, hallucination, and “reasoning” without mysticism
  • Align technical reality with business expectations

Product Leaders

Product managers and directors working with AI-powered features.

  • Build intuition for what models can and cannot reliably do
  • Make better tradeoffs between creativity and correctness
  • Avoid roadmap decisions driven by misleading AI language
  • Communicate AI capabilities clearly to non-technical stakeholders

And Isn't For

This book won't be the right fit if you're looking for quick fixes or surface-level answers.

Not for Tutorial Seekers

People looking for step-by-step tutorials or prompt recipes won't find them here. This book teaches the why, not the how.

Not for Hype Chasers

Readers who want hype, futurism, or AGI speculation won't find it here. This book is grounded in reality, not marketing.

Not for Quick Fixes

Teams seeking implementation patterns without understanding fundamentals won't find shortcuts. This book requires thinking.

This book is about thinking clearly, not shipping faster.

Raahul Seshadri
About the Author

Raahul Seshadri

I specialize in designing and architecting complex AI systems. As of early 2026, I lead AI for a major SaaS product—setting the vision, inventing frameworks, and packaging them into a wonderful product experience.

My background: I've been programming since the age of 11 (almost 25 years). I'm an undergraduate in Electronics & Telecommunications Engineering and hold a Master's in Computer Science from GeorgiaTech, specializing in Machine Learning.

Over the years, I've architected complex high-scale systems, led big teams, designed differentiators, and seen how business & technology intertwine behind closed room meetings with prominent leaders.

In writing this book, I combine my AI knowledge with my first-principles thinking approach—putting everything in the context of the business of technology so what I cover is both clear and useful.

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Frequently Asked Questions

Common questions about the book, what you'll learn, and who it's for

Who should read this book?
This book is designed for engineering leaders at all levels (from engineering managers to CTOs) who want to understand AI before making bets. It's also valuable for tech leads, architects, startup founders, data science leaders, and product leaders who need clear mental models of AI.
Do I need a technical background to understand the book?
A basic understanding of software engineering is helpful, but you don't need to be an AI or ML expert. The book explains concepts clearly without requiring a math degree or research background. If you build systems or make technical decisions, you'll understand this book.
What makes this book different from other AI books?
This book isn't about implementation details, tutorials, or hype. It's about building accurate mental models of how AI actually works. It's written for engineering leaders who need to reason clearly about AI, without vendor narratives, AGI speculation, or cargo-cult explanations.
How deep does the book go into technical details?
The book strikes a balance between simplicity and accuracy. You'll learn what tokens are, how training works, why models hallucinate, and how "reasoning" emerges, without needing to understand the math behind gradient descent. It's technically accurate but accessible to engineering leaders.
What will I be able to do after reading this book?
You'll be able to reason about AI rather than just using it. You'll know when an LLM is the right tool, predict how it will behave, understand where hallucinations come from, make sense of "reasoning" without mysticism, and design AI features instead of poking at them.
Can my whole team read this together?
Absolutely. The book is designed to be accessible to engineers at all levels, from senior engineers to managers to CTOs. Team licenses are available with tiered pricing. Reading it together can help align your team on mental models and set realistic expectations.
Does the book cover specific tools or platforms?
No, this book is tool-agnostic. It focuses on fundamentals that apply regardless of whether you use GPT, Claude, open-source models, or future AI systems. The principles you learn will remain relevant even as the technology landscape changes.
What's the author's background in AI?
Raahul Seshadri specializes in designing and architecting complex AI systems. He leads AI for a major SaaS product, has almost 25 years of programming experience, holds a Master's in Computer Science from Georgia Tech specializing in Machine Learning, and has been featured in multiple publications.
What formats are included?
The book is available in digital format only. You'll receive the eBook in DRM-free PDF format.