How to AI

👋 This handbook is based on the following resources and my own experience (still new). I encourage you to explore the original sources for deeper insights and broader context.

Primarily:

• AI Engineering by Chip Huyen

• AI Product Manager's Handbook by Irene Bratsis

Others:

• Building effective agents by Anthropic

• The 2025 AI Engineer Reading List by Latent Space

• 12-Factor Agents - Principles for building reliable LLM applications

• Zero to One: Learning Agentic Patterns by Phil Schmid

• From Software Engineer to AI Engineer by Gergely Orosz

• AI Engineer YouTube channel by World's Fair - tons of real world use cases

• AI Engineering Anti-patterns by Chip Huyen

Pre-reading:

• ☕ For super basic concepts, check out AI Agents Roadmap by Roadmap.sh

• 😄 For a non-tech intro to LLMs, check out Intro to Large Language Models by Andrej Karpathy

• 😍 For a tech-ish deep dive, check out Deep Dive into LLMs like ChatGPT by Andrej Karpathy

This handbook is written with the help of Cursor AI with vibe coding and NotebookLM.

Note: As someone new to AI Engineering myself, this handbook represents my public learning journey - Jamie Wen

Before we start, here are two diagrams about cognitive bias and hype cycle. I think they are quite interesting for this journey.

Dunning-Kruger Effect Curve

Gartner Hype Cycle

🦉, let's get started.