Course Outline
Grasping the Tools and Initiating a Build
How AI Coding Tools Function
Key topics include:
• Comprehending context windows and their inherent limitations
• Statelessness and how AI models manage information during sessions
• The Plan → Execute → Review workflow
• Capabilities and common challenges of AI coding tools
• Best practices for effective collaboration with AI assistants
The AI Coding Landscape
Key topics include:
• An overview of the current AI coding ecosystem
• Differentiating between tools like Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams
Anatomy of Effective Prompts
Key topics include:
• Core elements of a high-quality prompt
• Providing context and clearly defining tasks
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Initial Coding: Building From Scratch
Key topics include:
• Developing a project starting from an empty folder
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configurations
• Iteratively refining generated code
• Testing and perfecting the final solution
Existing Codebases, Customisation, and Review
Navigating a Codebase
Key topics include:
• Exploring and understanding unfamiliar codebases
• Using AI tools to query and analyse existing projects
• Mapping application structures and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding into established projects
Everyday Tasks: Fixing, Features, and Testing
Key topics include:
• Utilising AI tools to diagnose and resolve bugs
• Implementing new features and enhancements
• Creating and improving automated tests
• Validating AI-generated code and changes
• Boosting productivity in routine development tasks
Personalisation: Concept and Application
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identifying where and when personalisation mechanisms are relevant
• Best practices for configuring AI assistants
• Overview of advanced implementation methods
Guardrails, Risks, and Professional Judgement
Key topics include:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Identifying prompt injection and security risks
• Determining which tasks can be delegated to AI
• Applying human judgement and maintaining accountability in software development
Requirements
No previous coding or AI tooling experience is necessary.
Basic familiarity with code or Git is advantageous
A licensed account (Claude Code \/ Cursor \/ Copilot)
Target Audience:
This course is intended for individuals new to AI-assisted development, including non-coders, casual coders, and professionals in technical-adjacent roles such as QA, data, product, or operations. No prior development background is assumed.
Testimonials (2)
Considerations about privacy and policy
Angelo Stefano Soglia - Parkino srl
Course - Claude AI for Workflow Automation and Productivity
Learning how to prompt Claude and use it to digest all of the data I have available.
