Part 1: Foundations

Master the core mechanics of working with LLMs: Tokens, Context Windows, and Prompting Techniques.

What You'll Learn

This section covers the essential building blocks for working with AI APIs. Before you can build production-ready AI features, you need to understand how LLMs actually process text, how to communicate with them effectively, and how to control their behavior.

Lesson 1: Tokens & Context

Stop thinking in words and start thinking in tokens—the fundamental unit that LLMs process and you pay for. You'll learn why a simple character count can mislead you into budget overruns or context window crashes. We'll use tiktoken to calculate exact costs before sending requests and understand the hard limits imposed by context windows. By the end, you'll know how to budget tokens programmatically and avoid the dreaded "context length exceeded" error that breaks production systems.

Lesson 2: Crafting Prompts That Actually Work

Transform prompts from vague wishes into engineered code. You'll master three fundamental patterns: Zero-Shot (when the task is self-explanatory), Few-Shot (teaching by example), and Chain-of-Thought (forcing the model to reason step-by-step). We'll also cover delimiters—the fences that prevent prompt injection attacks and keep user data separate from instructions. This lesson shifts your mindset from "hoping the AI understands" to "engineering predictable behavior."

Lesson 3: System Prompts

Learn to program AI behavior at the architectural level. While user prompts handle individual requests, system prompts define persistent behavior across entire conversations—they're the employee handbook that shapes every interaction. You'll structure system prompts using identity, context, rules, format, and guidance sections. We'll explore dynamic injection (current date, user preferences), baked-in reasoning patterns, and defensive techniques to prevent adversarial users from breaking your bot's constraints. By the end, you'll write system prompts that remain robust under pressure.

Lesson 4: Generation Parameters

Master the knobs that control creativity versus precision. Even perfect prompts fail if you don't configure generation parameters correctly. You'll understand how temperature scales randomness, how Top-p and Top-k sampling methods filter tokens, and how frequency/presence penalties prevent repetitive outputs. We'll cover the probability engine that powers token selection and show you when to use creative settings (marketing copy) versus deterministic ones (JSON extraction). This lesson gives you fine-grained control over model behavior without changing a single word in your prompts.

Why This Matters

These four lessons form the foundation of production AI engineering. Without understanding tokens, you'll waste money and hit unexpected limits. Without mastering prompts, you'll build unreliable systems. Without system prompts, your AI will lack consistency. Without generation parameters, you'll struggle to control output quality.

Complete these lessons before moving to API integration in Part 2.