Course Outline
Understanding the Tools and Initial Build
How AI Coding Tools Operate
Key concepts include:
• Understanding context windows and their inherent limitations
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• Identifying areas where AI coding tools excel and where they face challenges
• Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key concepts include:
• An overview of the current AI coding landscape
• Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Evaluating the strengths and constraints of different coding assistants
• Practical advice for adopting tools within development teams
Deconstructing Prompts
Key concepts include:
• Essential components of high-quality prompts
• Providing clear context and task definitions
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
First Coding Experience: Building from Scratch
Key concepts include:
• Creating a project from an empty directory
• Establishing initial application structure and scaffolding
• Managing dependencies and project settings
• Iteratively refining generated code
• Testing and perfecting the final solution
Existing Codebases, Personalisation, and Review
Navigating Existing Codebases
Key concepts include:
• Exploring and comprehending unfamiliar codebases
• Using AI tools to query and analyse existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding into established projects
Routine Tasks: Debugging, Feature Development, and Testing
Key concepts include:
• Leveraging AI tools to investigate and resolve bugs
• Developing new features and enhancements
• Creating and optimising automated tests
• Validating AI-generated code and changes
• Boosting productivity in daily development activities
Personalisation: Core Concepts
Key concepts include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identifying when and where personalisation mechanisms are effective
• Best practices for configuring AI assistants
• Overview of advanced implementation methods
Guardrails, Risks, and Critical Thinking
Key concepts include:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognising prompt injection and security vulnerabilities
• Determining which tasks can be safely delegated to AI
• Applying human judgement and maintaining accountability in software development
Requirements
Previous coding or AI tool experience is not necessary.
A basic understanding of code or Git is advantageous
A licensed account (for Claude Code, Cursor, or Copilot)
Target Audience:
Individuals new to AI-assisted development, including non-technical staff and occasional coders, as well as 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.
