Understanding AI-Assisted Development

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Master the principles behind AI-assisted development so you can build sustainable workflows for any tool or team.

Guide, Judge, and Correct AI-Generated Code
The software development industry always changes. But nothing compares to what’s happening right now with AI-assisted development.

There a lot of AI users now who copy prompts and hope for the best. There are also worried devs, who keep hearing that AI will replace them or that they’ll lose their skills if they lean on it too much.

A proper view of AI is that it isn’t a replacement for understanding. It’s a tool that amplifies it. But it can also amplify you and your team’s worst qualities.

The real skill isn’t using AI. It’s knowing how to think with it.

This course does not teach you the latest AI tool, because by next month there’ll be a new one. Instead, I want to build something more permanent in your mind: a framework for understanding how large language models actually work, so you can then follow workflows and best practices that are strategic, reliable, and yours.

This course will teach you and your team how to successfully integrate LLMs across your entire software development lifecycle.

What Makes This Different
Most AI courses focus on prompts. Copy this, paste that, hope it works. But that’s not how the best AI-assisted developers operate. You need to understand the underlying principles, the same way understanding JavaScript’s execution context makes you better at debugging, or knowing how React’s reconciliation works helps you write more performant components.

This course goes under the hood, hype-free. We’ll explore the math and linguistics that make LLMs tick. You’ll learn about vector spaces, attention mechanisms, and why models confabulate (what people incorrectly call “hallucinations”). We’ll look at context engineering, not just prompt engineering, because managing what the model “knows” is more important than how you ask.

And yes, we’ll cover practical workflows: planning, implementation, debugging, code reviews. But you’ll understand why these workflows work, which means you can adapt them as tools change.

This Is For You If…
You’re a beginner who wants to use AI without feeling like you’re cheating yourself out of learning. You’ll discover how to leverage AI while still building the mental models that make you valuable as a developer.

You’re an experienced developer who wants to integrate AI into your workflow (or your team’s) without sacrificing code quality. You’ll learn where AI excels, where it fails, and how to structure your development process to get consistent, maintainable results.

You’re worried about becoming dependent on AI or losing your edge. I address this directly. I’ll show you how to use AI as a force multiplier for your expertise, not a crutch that weakens it, and how to avoid both the personal and professional pitfalls of AI-assisted development.

What You’ll Walk Away With
By the end, you’ll have a durable mental model of how LLMs work. You’ll know how to engineer context and prompts strategically. You’ll understand human-AI collaboration patterns that actually work in production codebases.

Most importantly, you’ll stop feeling uncertain. You’ll approach AI tools with clarity, purpose, and the confidence that comes from genuine understanding.

Because at the end of the day, the developers who thrive aren’t the ones who found the best prompts. They’re the ones who understand what they’re doing.

In the age of AI it’s even more important to not imitate, but understand.

Table of Contents
1 Introduction
2 Setup
3 A Proper Mental Model of LLMs
4 Large Language Models and Grammar
5 Conceptual Aside Vectors
6 Attention and Attending
7 Conceptual Aside Determinism vs Non-Determinism
8 Determinism and the Digital Age
9 Conceptual Aside Programming Language Grammar
10 Prediction and Statistics
11 Confabulation and Unreliability
12 Conceptual Aside Reasoning Models
13 Conceptual Aside Agents
14 You Aren’t Having a Conversation (and the Dangers of Anthropomorphization)
15 Context Engineering and Management
16 Pattern Matching and Navigating the Embedding Space
17 Is It Engineering_
18 Project Context
19 Technical Context
20 Context Refresh and Drift
21 Immediate Context
22 Task Context
23 Clean Human Code
24 Agents and Context
25 Prompt Engineering
26 The Anatomy of Effective Prompts
27 Decomposition
28 Roles and Personas
29 Specificity and Constraints
30 Examples of Expected Behavior
31 Session Context
32 Code Generation Workflows Planning
33 Brainstorming
34 Business Rules and Constraints
35 Documentation
36 Implementation Planning
37 The Context Problem
38 Conceptual Aside Context Window
39 Window Size
40 Conceptual Aside System Prompt
41 Context Rot
42 Conceptual Aside Markdown
43 Skills
44 The Anatomy of a Skill
45 Frontmatter
46 Instructions
47 Scripts
48 Assets
49 How Agents Integrate Skills
50 Conceptual Aside Progressive Disclosure
51 Discover
52 Load Metadata
53 Match Tasks to Skills
54 Activate
55 Execute and Access
56 Skills In Action
57 Skill Authoring
58 Metadata
59 Good Context
60 Domain Expertise
61 New Capabilities
62 Repeatable Workflows
63 Interoperability
64 Finding Pre-Existing Skills
65 Skills Project
66 Code Generation Workflows Implementation
67 Task Decomposition
68 Rules
69 Code Constraints (Types, Tests, and Patterns)
70 The Model Context Protocol
71 Agent Skills
72 Agent Orchestration
73 Code Generation Workflows Integration
74 Human-In-The-Loop
75 AI-Assisted Debugging
76 Iterative Refinement and Not Breaking What’s Working
77 Team Collaboration When AI is a Team Member
78 Quality Control
79 Established Patterns
80 Explainability
81 Systems Integration
82 Edge Cases
83 Performance
84 AI Pitfalls and How to Manage Them
85 Hallucinations
86 A Lack of Training Data (i.e. New Things)
87 The Echo Chamber Effect
88 Cognitive Load
89 A Stranger to Your Codebase
90 Maintainability
91 Context Switching
92 Cognitive Laziness and Maintaining Your Skill
93 Losing The Joy of Coding
94 Don’t Imitate, Understand
95 Practical Tooling
96 IDEs and Completions
97 Conversations
98 Agents and Iteration
99 Hype and Selecting Tools
100 Capstone Project
101 Plan
102 Implement
103 Integrate
104 Quality Control
105 Conclusion