AI Coding Course

Lesson 1 • What AI Coding Tools Actually Do

Explains the mechanics behind large language models used for code generation. Grounds all later tool usage in accurate conceptual understanding.

Lesson 2 • Reading and Trusting AI-Generated Code

Teaches critical evaluation of AI output before accepting suggestions. Establishes a verification habit that prevents compounding errors.

Lesson 3 • Landscape of AI Coding Assistants

Surveys the major categories of AI coding tools available today. Helps students choose the right tool for each task type.

Lesson 1 • Avoiding Common Prompt Mistakes

Identifies the most frequent prompting errors that produce broken or insecure code. Prevents bad habits before they become ingrained.

Lesson 2 • Iterative Prompting and Refinement

Covers multi-turn conversations to progressively improve generated code. Builds the habit of treating AI as a collaborative partner, not a one-shot oracle.

Lesson 3 • Context Management in Long Sessions

Explains how context window limits affect code quality in extended sessions. Teaches strategies to keep AI output coherent across large codebases.

Lesson 4 • Prompt Patterns for Common Coding Tasks

Provides reusable prompt templates for frequent development scenarios. Accelerates daily workflow by reducing prompt-writing time.

Lesson 1 • Generating Boilerplate for APIs and Services

Focuses on scaffolding REST and service layer code using AI prompts. Prepares students for backend development acceleration.

Lesson 2 • Building Classes and Data Models

Teaches AI-assisted creation of object-oriented structures and data schemas. Extends generation skills to larger code units.

Lesson 3 • Validating and Integrating Generated Code

Establishes a workflow for safely merging AI-generated code into an existing codebase. Prevents integration bugs from accumulating.

Lesson 4 • Project Scaffolding and Boilerplate

Uses AI to generate full project directory structures and configuration files. Eliminates repetitive setup work at the start of every project.

Lesson 1 • Performance and Memory Issue Diagnosis

Uses AI to identify slow code paths and memory inefficiencies. Adds performance debugging to the AI-assisted toolkit.

Lesson 2 • Understanding AI Error Explanations

Covers how to interpret and validate AI explanations of bugs. Builds debugging intuition alongside AI assistance.

Lesson 3 • Debugging Logic Errors with AI

Addresses non-exception bugs where output is wrong but no error is thrown. Extends AI debugging beyond simple error messages.

Lesson 4 • Feeding Errors to AI Effectively

Teaches how to format error messages and stack traces for AI analysis. Maximises the accuracy of AI-generated fixes.

Lesson 1 • AI-Assisted Code Review

Uses AI to surface bugs, style violations, and design issues in existing code. Complements human review with automated depth.

Lesson 2 • Enforcing Coding Standards with AI

Teaches AI-assisted enforcement of team style guides and best practices. Reduces review friction and maintains consistency.

Lesson 3 • Refactoring Code with AI Guidance

Covers using AI to restructure code without changing behavior. Builds confidence in large-scale refactoring tasks.

Lesson 1 • Improving Test Coverage Strategically

Uses AI to analyse coverage gaps and prioritise which tests to add. Connects test generation to measurable quality outcomes.

Lesson 2 • Generating Unit Tests with AI

Covers prompting AI to produce unit tests for existing functions and classes. Directly accelerates the most common testing task.

Lesson 3 • Validating and Maintaining AI-Generated Tests

Teaches how to verify that generated tests are correct and maintainable. Prevents a false sense of security from low-quality tests.

Lesson 4 • Integration and End-to-End Test Scaffolding

Extends AI test generation to multi-component and full-flow scenarios. Prepares students for testing realistic system interactions.

Lesson 1 • Configuring and Running Coding Agents

Covers practical setup and execution of agent-based coding tools. Gives students hands-on control over agent behavior.

Lesson 2 • Designing Effective Agent Task Prompts

Teaches how to write high-level task descriptions that agents can execute reliably. Extends prompt engineering skills to autonomous contexts.

Lesson 3 • Understanding AI Coding Agents

Explains how agent-based systems plan and execute multi-step coding tasks. Distinguishes agents from simple chat-based assistants.

Lesson 4 • Automating Repetitive Dev Tasks with Agents

Applies agents to recurring tasks like migrations, refactors, and boilerplate updates. Demonstrates compounding productivity gains from automation.

Lesson 1 • Evaluating and Adopting New AI Tools

Provides a framework for assessing new AI coding tools as the market evolves. Keeps students adaptive as the tooling landscape changes rapidly.

Lesson 2 • Integrating AI into the Development Lifecycle

Maps AI tool usage to each phase of a standard development lifecycle. Ensures AI augments rather than disrupts existing team processes.

Lesson 3 • Building a Personal AI Coding Workflow

Guides students in designing a repeatable, personalised AI-assisted coding routine. Converts individual techniques into a coherent daily system.

Lesson 4 • Managing AI Tool Costs and Rate Limits

Covers strategies for controlling API usage costs and avoiding rate limit disruptions. Ensures AI tools remain economically sustainable in production.