Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
• Grasping context windows and their inherent limitations
• Statelessness and how AI models retain session information
• The Plan → Execute → Review workflow
• Identifying strengths and weaknesses 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
• Selecting appropriate models and tools for specific tasks
• Evaluating the strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams
Prompt Anatomy
Key topics include:
• Essential components of a high-quality prompt
• Clearly providing context and defining the task
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Strategies to enhance prompt quality and consistency
First Coding: Build From Scratch
Key topics include:
• Building a project starting from an empty folder
• Creating initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and perfecting the final solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Key topics include:
• Navigating and comprehending an unfamiliar codebase
• Querying and analyzing existing projects with AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Speeding up onboarding into existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
• Leveraging AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating AI-generated code and changes
• Boosting productivity in routine development tasks
Personalisation: What It Is
Key topics include:
• 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
• Overview of advanced implementation strategies
Guardrails, Risks and Judgement
Key topics include:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks can be delegated to AI
• Applying human judgment and maintaining accountability in software development
Requirements
No previous experience with coding or AI tools is necessary.
Basic familiarity with code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot.
Audience:
Individuals new to AI-assisted development, including non-coders, occasional coders, and professionals in technical-adjacent roles such as QA, data, product, or operations. No prior development background is expected.
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.
