Course Outline
Grasping the Tools and Initial Build
The Mechanics of AI Coding Tools
Key topics include:
• Understanding context windows and their inherent limitations
• Statelessness and how AI models maintain information within a session
• The Plan → Execute → Review workflow
• Identifying tasks where AI excels and where it falls short
• Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
• An overview of the current AI coding landscape
• Understanding the distinctions between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for specific tasks
• Evaluating the strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams
Structure of Effective Prompts
Key topics include:
• Essential components of high-quality prompts
• Providing clear context and task definitions
• Defining output formats and constraints
• Common prompting frameworks and templates
• Techniques to enhance prompt quality and consistency
Initial Coding: Building from Scratch
Key topics include:
• Constructing a project starting from an empty folder
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and polishing the final solution
Existing Codebases, Personalization, and Review
Working Within a Codebase
Key topics include:
• Navigating and understanding unfamiliar codebases
• Querying and analyzing existing projects with AI tools
• Mapping application structures and dependencies
• Generating documentation and technical summaries
• Speeding up the onboarding process for existing projects
Day-to-Day Tasks: Fixing, Feeding, and Testing
Key topics include:
• Using AI tools to diagnose and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Verifying generated code and changes
• Boosting productivity in routine development tasks
Personalization: An Introduction
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and the concept of project memory
• Determining when and where personalization mechanisms are applicable
• Best practices for configuring AI assistants
• An overview of advanced implementation strategies
Guardrails, Risks, and Judgment
Key topics include:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
No previous coding experience or familiarity with AI tools 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 programmers, and professionals in technical-adjacent fields such as QA, data, product, or operations. No 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.
