Course Outline
Grasping the Tools and Initial Development
The Mechanics Behind AI Coding Tools
Key concepts addressed:
• Context windows and their inherent limitations
• Statelessness and the retention of information by AI models during a session
• The Plan → Execute → Review workflow
• Capabilities and challenges of AI coding tools
• Strategies for effective collaboration with AI assistants
The AI Coding Ecosystem
Key concepts addressed:
• An overview of the current AI coding landscape
• Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
• Choosing appropriate models and tools for specific tasks
• The strengths and limitations of various coding assistants
• Practical advice for integrating these tools into development teams
Anatomy of Effective Prompts
Key concepts addressed:
• Essential elements of a high-quality prompt
• Clarifying context and defining the task
• Defining output formats and constraints
• Common prompting frameworks and templates
• Methods for enhancing prompt quality and consistency
Initial Coding: Creating a Project from Scratch
Key concepts addressed:
• Developing a project starting from an empty directory
• Establishing the initial application structure and scaffolding
• Handling dependencies and project settings
• Refining generated code through iteration
• Testing and polishing the final output
Working with Existing Code, Customization, and Review
Navigating a Codebase
Key concepts addressed:
• Exploring and understanding unfamiliar codebases
• Using AI tools to query and analyze existing projects
• Mapping out application structure and dependencies
• Generating documentation and technical summaries
• Speeding up the onboarding process for existing projects
Routine Tasks: Debugging, Features, and Testing
Key concepts addressed:
• Leveraging AI tools to diagnose and resolve bugs
• Developing new features and improvements
• Creating and enhancing automated tests
• Verifying generated code and modifications
• Boosting efficiency in daily development activities
Customization: The Basics
Key concepts addressed:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Application of customization mechanisms in relevant contexts
• Best practices for setting up AI assistants
• An overview of advanced implementation strategies
Guardrails, Risks, and Critical Thinking
Key concepts addressed:
• Reviewing and validating AI-generated code
• Understanding common failure patterns and limitations
• Identifying prompt injection and security threats
• Determining which tasks can be delegated to AI
• Exercising human judgment and maintaining accountability in software development
Requirements
No previous coding or AI tool experience is necessary.
Basic knowledge of code or Git is beneficial
A licensed account (Claude Code / Cursor / Copilot)
Target Audience:
Individuals new to AI-assisted development, including non-coders, occasional coders, and professionals in technical-related roles such as QA, data, product, or operations. No prior development experience 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.