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 inherent limitations
• Grasping statelessness and how AI models retain information during a session
• Exploring the Plan → Execute → Review workflow
• Identifying where AI coding tools excel and where they fall short
• Adopting best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Landscape
Topics covered:
• A comprehensive overview of the current AI coding ecosystem
• Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Recognising the strengths and limitations of various coding assistants
• Practical advice on integrating these tools into development teams
Module 3 | Prompt Anatomy
Topics covered:
• The essential components of an effective prompt
• Providing clear context and defining the task precisely
• Specifying output formats and constraints
• Familiarity with 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 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 comprehending an unfamiliar codebase
• Querying and analysing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating the onboarding process for existing projects
Module 6 | Everyday Tasks: Fix, Feature and Test
Topics covered:
• Leveraging 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 activities
Module 7 | Personalisation: What It Is
Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identifying where and when personalisation mechanisms apply
• Best practices for configuring AI assistants
• An 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
• Determining which tasks can be safely delegated to AI
• Applying human judgement and maintaining accountability in software development
Requirements
No prior coding knowledge or experience with AI tools is necessary.
A basic understanding of code or Git is advantageous.
Requires an active licensed account for Claude Code, Cursor, or Copilot.
Target Audience:
This course is ideal for individuals new to AI-assisted development, including non-programmers, occasional coders, and technical-adjacent professionals in QA, data analytics, 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
