Course Outline
Day 1 | Understanding the Tools and a First Build
Module 1 | How AI Coding Tools Actually Work
Topics covered:
• Comprehending context windows and their constraints
• Statelessness and how AI models maintain information during a session
• The Plan → Execute → Review workflow
• Areas where AI coding tools excel and where they face challenges
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Landscape
Topics covered:
• A survey of the current AI coding ecosystem
• Distinctions between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Strengths and limitations of various coding assistants
• Practical advice on adopting tools within development teams
Module 3 | Prompt Anatomy
Topics covered:
• Essential components of an effective prompt
• Providing context and clearly defining tasks
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing the quality and consistency of prompts
Module 4 | First Coding: Build From Scratch
Topics covered:
• Constructing a project starting from an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and polishing the final solution
Day 2 | Existing Codebases, Personalisation and Review
Module 5 | Working in a Codebase
Topics covered:
• Navigating and understanding unfamiliar codebases
• Querying and analyzing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into existing projects
Module 6 | Everyday Tasks: Fix, Feature and Test
Topics covered:
• Using AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and modifications
• Boosting productivity in daily development tasks
Module 7 | 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
Module 8 | 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 coding or AI-tool experience is necessary.
Basic familiarity with code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
This course is designed for those new to AI-assisted development, including non-coders, occasional programmers, and technical-adjacent professionals in QA, data, product management, or operations. No prior development background is assumed.
Testimonials (2)
Learning how to prompt Claude and use it to digest all of the data I have available.
Mike Hartleroad - Furniture Row
Course - Claude AI for Data Analysis and Business Intelligence
how to engage with the Office environment and set up repetitive tasks
