What You Will Learn
Day 1: How Language Models Actually Work Before you write a single prompt you need an accurate mental model of what you are working with. You will learn how tokens, context windows, and probability distributions shape every output you receive, why the model has no memory of your intent and only responds to what you write, and why prompt structure changes model behavior in ways that are predictable and controllable once you understand the mechanics.
Day 2: Prompt Anatomy and Core Techniques You will learn the four components every well-formed prompt contains, how to use zero-shot, one-shot, and few-shot prompting to control output quality and consistency, how role prompting and persona setting activate domain-level expertise and precise tone, and how negative prompting closes the gap between an output that is almost right and one that is exactly right.
Day 3: Reasoning and Complex Tasks You will learn how chain-of-thought prompting dramatically improves accuracy on multi-step tasks by making the model’s reasoning visible and auditable, how step-back prompting grounds complex answers in principled reasoning rather than surface pattern matching, how to decompose complex tasks into sequential stages that compound quality across every step, and when reasoning chains add value versus when a direct answer is the faster and smarter choice.
