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 Duration 14 hours

Course Outline

Understanding the Tools and a First Build

How AI Coding Tools Actually Work

Topics covered:
• Understanding context windows and their limitations
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• What AI coding tools can do well and where they struggle
• Best practices for collaborating effectively with AI assistants

The AI Coding Landscape

Topics covered:
• Overview of the current AI coding ecosystem
• Understanding the differences between tools such as Cursor, GitHub Copilot and Claude Code
• Selecting the right model and tool for different tasks
• Strengths and limitations of various coding assistants
• Practical recommendations for tool adoption within development teams

Prompt Anatomy

Topics covered:
• The key components of an effective prompt
• Providing context and defining the task clearly
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for improving prompt quality and consistency

First Coding: Build From Scratch

Topics covered:
• Building a project from an empty folder
• Creating the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively improving the generated code
• Testing and refining the final solution

Existing Codebases, Personalisation and Review

Working in a Codebase

Topics covered:
• Navigating and understanding an unfamiliar codebase
• Querying and analysing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into existing projects

Everyday Tasks: Fix, Feature and Test

Topics covered:
• Using AI tools to investigate and fix bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and changes
• Increasing productivity in day-to-day development tasks

Personalisation: What It Is

Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Where and when personalisation mechanisms apply
• Best practices for configuring AI assistants
• Overview of advanced implementation approaches

Guardrails, Risks and Judgement

Topics covered:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognising prompt injection and security risks
• Deciding what work can be delegated to AI
• Applying human judgement and maintaining accountability in software development

Requirements

No previous experience with coding or AI tools is necessary.

Basic knowledge of code or Git is beneficial.

A licensed account for Claude Code, Cursor, or Copilot is required.

Target Audience:

This course is suitable for individuals new to AI-assisted development, encompassing non-coders, occasional developers, and professionals in adjacent technical fields such as QA, data, product, or operations. No prior development experience is assumed.

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