AI Prompt Course

Lesson 1 • The Prompt-Response Relationship

Defines a prompt and maps the input-output loop between user and model. Shows how context window limits shape what the model can consider at once.

Lesson 2 • How Large Language Models Work

Explains token prediction, training data, and probability-based output generation. Establishes the mechanical basis that explains why prompt wording changes model behaviour.

Lesson 3 • Core Prompting Vocabulary

Introduces essential terms used throughout the course: instruction, role, context, format, and constraint. Shared vocabulary prevents misunderstanding in later technical sections.

Lesson 1 • Assigning Roles and Personas

Teaches how role assignment shifts model tone, expertise level, and perspective. Connects to task definition by aligning the model's voice with the intended output.

Lesson 2 • Adding Constraints and Guardrails

Shows how negative instructions and boundary rules prevent unwanted content. Completes the full prompt structure introduced across this chapter.

Lesson 3 • Defining the Task Clearly

Covers how verb choice, specificity, and scope determine task clarity. Directly impacts whether the model attempts the right action.

Lesson 4 • Specifying Output Format

Demonstrates how format instructions control structure, length, and style of responses. Reduces post-processing effort and increases output usability.

Lesson 1 • Chain-of-Thought Prompting

Introduces step-by-step reasoning instructions that improve accuracy on complex tasks. Connects to few-shot by showing how reasoning examples amplify the technique.

Lesson 2 • Instruction Layering and Sequencing

Demonstrates how to stack multiple instructions in logical order for complex outputs. Prevents instruction conflicts that arise when combining techniques.

Lesson 3 • Zero-Shot Prompting

Covers direct instruction without examples and identifies tasks where it succeeds or fails. Establishes the baseline technique before introducing example-based methods.

Lesson 1 • Systematic Prompt Editing Strategies

Introduces one-variable-at-a-time editing to isolate what causes output changes. Prevents the common mistake of changing multiple elements simultaneously.

Lesson 2 • Handling Refusals and Safety Filters

Explains why models refuse requests and how to reframe prompts ethically to achieve legitimate goals. Distinguishes productive reframing from policy circumvention.

Lesson 3 • A/B Testing Prompt Versions

Applies controlled comparison methods to evaluate which prompt version performs better. Introduces objective scoring criteria for consistent evaluation.

Lesson 1 • Meta-Prompting and Self-Refinement

Uses the model to critique and improve its own output through structured feedback loops. Reduces manual iteration by automating the refinement process.

Lesson 2 • Self-Consistency and Majority Voting

Generates multiple independent responses and selects the most consistent answer. Improves reliability on reasoning tasks where single-pass output is unreliable.

Lesson 3 • Prompt Chaining for Complex Workflows

Connects sequential prompts where each output feeds the next input. Enables multi-step tasks that exceed single-prompt capability.

Lesson 4 • Tree-of-Thought Prompting

Structures model reasoning as branching decision paths rather than linear steps. Enables exploration of multiple solution routes before committing to an answer.

Lesson 1 • Prompting for Professional Writing

Applies role, tone, and format controls to produce emails, reports, and marketing copy. Demonstrates how writing-specific constraints improve output consistency.

Lesson 2 • Prompting for Data Analysis Tasks

Teaches how to describe datasets, specify analysis goals, and request structured output. Enables non-programmers to extract analytical value through precise prompting.

Lesson 3 • Prompting for Code Generation

Covers language specification, function scope, and test inclusion in code prompts. Connects chain-of-thought techniques to step-by-step code reasoning.

Lesson 1 • Privacy and Data Handling in Prompts

Covers risks of including personal or sensitive data in prompts and mitigation strategies. Applies to both individual use and organisational deployment contexts.

Lesson 2 • Responsible Deployment Principles

Establishes a framework for evaluating whether a prompting application is appropriate to deploy. Synthesises all ethical concepts into actionable deployment decision criteria.

Lesson 3 • Bias and Fairness in Prompt Design

Examines how prompt wording can amplify or reduce model bias in outputs. Teaches proactive bias auditing as a standard step in prompt development.

Lesson 1 • System Prompt Architecture

Explains the role of system prompts in setting persistent behaviour, tone, and constraints. Distinguishes system-level from user-level instructions and their interaction.

Lesson 2 • Building Reusable Prompt Templates

Introduces variable placeholders and modular template design for repeatable tasks. Reduces prompt creation time and enforces consistency across team members.

Lesson 3 • Prompt Libraries and Management

Covers organising, tagging, and retrieving prompts in a structured library. Enables teams to build institutional knowledge rather than recreating prompts repeatedly.