Week 2: How AI Coding Actually Works — Slides Outline

"The hottest new programming language is English." — Andrej Karpathy

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Slide 1: Title

Week 2: How AI Coding Actually WorksTokenization, Context, Prompts, and Tools

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Slide 2: Pulse Check (Start)

Before we begin, rate your confidence (1-5):

1. Do you understand how AI models process your code?
2. How would you explain "context window" to a friend?

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Slide 3: Learning Objectives

By the end of this week, you will:

1. Understand tokenization, context windows, and next-token prediction
2. Analyze AI coding tools across categories and tradeoffs
3. Apply prompt engineering and context engineering techniques
4. Evaluate AI-generated code for quality and maintainability

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Slide 4: Tokenization — How AI Reads Your Code

AI doesn't see words or characters—it sees tokens (subword chunks)

"function"              → [function]                    = 1 token
"XMLHttpRequest"        → [XML] [Http] [Request]        = 3 tokens (3x cost!)
const greeting = "Hello World";
  → [const] [_greeting] [_=] [_"] [Hello] [_World] [";] = 7 tokens

Key Insight: Common words = fewer tokens. Rare/compound words = more tokens.

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Slide 5: Context Windows — Why AI "Forgets"

Model	Context	Notes
Claude 3.5 Sonnet	200K tokens	Industry standard
Gemini 2.5 Pro	1M tokens	5x larger
GPT-4	128K tokens	Strong reasoning
Local models	8K–32K	Resource-constrained

200K tokens ≈ 150,000 words ≈ 300 pages of text

Engineering implication: Start fresh sessions for new features. The AI isn't choosing to forget—the architecture can't see earlier messages once context is full.

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Slide 6: Temperature & Sampling

Temperature = how random the model is.

Temperature = 0.0    Deterministic (always same output)
            0.3    Consistent (minor variations)
            0.7    Balanced (some creativity)
            1.0+   Creative/Chaotic

When to use:

• Production code: 0.0–0.3 (consistency)
• Brainstorming: 0.7–1.0 (creativity)

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Slide 7: Next-Token Prediction

An LLM doesn't think. It predicts the next token repeatedly.

Input: "function add(a, b) {"
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Transformer: "What token should come next?"
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Output probabilities:
  " return" → 65%
  "\n  return" → 15%
  "const" → 3%
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Generate: "function add(a, b) { return"
         ↓
Repeat thousands more times...

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Slide 8: Why Hallucinations Happen

Hallucinations aren't bugs—they're features of next-token prediction.

The model has no "I'm not sure" mechanism. It's always confident.

Defense:

1. Know your domain
2. Always test the code
3. Validate APIs against docs
4. Human review required

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Slide 9: Where AI Excels

• Boilerplate (React components, API handlers, CRUD)
• Code transformation (refactoring, formatting)
• Generating from specifications (tests from code)
• Documentation (docstrings from signatures)
• Language translation (Python ↔ JavaScript)
• Explaining code

Why? High-pattern tasks with thousands of examples in training data.

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Slide 10: Where AI Struggles

• Novel algorithms (not in training data)
• Complex math (requires symbolic reasoning)
• Long chains of logic
• Security analysis (global perspective needed)
• Performance optimization (needs measurement)
• Guarantees & proofs

Rule of thumb: Use AI for "implement this pattern." Don't use AI for "solve this hard problem."

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Slide 11: AI Tool Categories

IDE-Native:

• Cursor, Windsurf, GitHub Copilot
• Best for: integrated editing, multi-file changes

Terminal-Native:

• Claude Code, Gemini CLI, aider
• Best for: backend, scripting, automation

Rapid Prototyping:

• Bolt.new, v0, Lovable, Replit
• Best for: MVPs, hackathons, learning

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Slide 12: Tool Selection Matrix

Task	Best Tool	Why
Boilerplate API	Cursor/Windsurf	IDE integration
Rapid MVP	Bolt.new	Fastest to live
Large refactor	Windsurf/Claude	Multi-file, terminal
Backend/scripting	Claude Code	No GUI needed
Cost-sensitive	Gemini CLI	Free + 1M context

The principle: Tools change every 3 months. Disciplines last decades.

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Slide 13: Prompt Engineering Evolution

Level 1 - Vague:

"Make a website"

Level 2 - Structured:

Create a React todo app with:
- Scope: CRUD, localStorage, dark mode
- Non-Goals: Backend, auth, real-time
- Constraints: <5 KB gzipped

Level 3 - Context Engineering:

[Provide codebase structure]
[Provide style examples]
[Provide constraint files]
Then: "Implement logout endpoint"

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Slide 14: Pulse Check (End)

Reflect on today's session:

1. What surprised you most about how AI generates code?
2. Which tool category fits your workflow best?

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Slide 15: What's Next

Week 3: From PRD to Deployed Prototype

• Ship something live
• Platform selection
• Debugging deployment failures

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Instructor: Goker Ezberci | gokerez@gmail.com