Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key areas of focus include:
• Grasping the concept of context windows and understanding their inherent limitations
• Exploring statelessness and how AI models manage information retention within a session
• Mastering the Plan → Execute → Review workflow
• Identifying the strengths of AI coding tools as well as the areas where they face challenges
• Adopting best practices for productive collaboration with AI assistants
The AI Coding Landscape
This section covers:
• A comprehensive overview of the current AI coding ecosystem
• Distinguishing between major tools such as Cursor, GitHub Copilot, and Claude Code
• Strategies for selecting the most suitable model and tool for specific tasks
• Evaluating the unique strengths and limitations of various coding assistants
• Practical advice for integrating these tools into development team workflows
Prompt Anatomy
Learn about:
• The essential components that make up a high-quality prompt
• Effectively providing context and clearly defining task requirements
• Setting clear output formats and constraints
• Utilizing common prompting frameworks and templates
• Methods for enhancing prompt quality and ensuring consistency
First Coding: Build From Scratch
Topics include:
• Initiating a project from an empty folder
• Establishing the initial application structure and scaffolding
• Handling dependencies and project configuration settings
• Iteratively refining the code generated by the AI
• Testing and perfecting the final solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Coverage includes:
• Navigating and deciphering unfamiliar codebases
• Leveraging AI tools to query and analyse existing projects
• Mapping out application structures and dependencies
• Generating documentation and creating technical summaries
• Speeding up the onboarding process for existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
• Utilizing AI tools to investigate and resolve bugs
• Developing new features and applying enhancements
• Crafting and optimizing automated tests
• Verifying generated code and assessing changes
• Boosting productivity in routine development tasks
Personalisation: What It Is
This part covers:
• Understanding project rules and configuration files
• An introduction to AGENTS.md and the concept of project memory
• Identifying where and when personalisation mechanisms are most effective
• Best practices for tailoring AI assistant configurations
• An overview of advanced implementation strategies
Guardrails, Risks and Judgement
Final topics include:
• Reviewing and validating code generated by AI
• Understanding common failure modes and system limitations
• Recognising prompt injection threats and other security risks
• Making informed decisions on which tasks to delegate to AI
• Applying human judgment and maintaining accountability in the development process
Requirements
No prior experience with coding or AI tools is necessary for participation.
However, a basic understanding of code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
This course is ideal for individuals new to AI-assisted development, including non-coders and those who code occasionally. It is also suitable for professionals in technical-adjacent fields such as QA, data analytics, product management, or operations, with no previous development background 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.
