Course Outline
Understanding the Tools and a First Build
The Mechanics of AI Coding Tools
Key topics include:
• Understanding context windows and their inherent limitations
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• Identifying tasks where AI coding tools excel and areas where they face challenges
• Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
• An overview of the current AI coding landscape
• Differentiating between tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for specific tasks
• Analyzing the strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams
Prompt Structure
Key topics include:
• Essential components of a high-quality prompt
• Providing context and clearly defining tasks
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Initial Coding: Building from Scratch
Key topics include:
• Creating a project from an empty directory
• Establishing 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 an Existing Codebase
Key topics include:
• Navigating and interpreting unfamiliar codebases
• Querying and analyzing existing projects using AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into existing projects
Everyday Tasks: Fixes, Features, and Testing
Key topics include:
• Utilizing AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Developing and improving automated tests
• Validating generated code and changes
• Boosting productivity in routine development tasks
Personalization: Concepts and Application
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identifying where and when personalization mechanisms are applicable
• Best practices for configuring AI assistants
• An overview of advanced implementation approaches
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 can be safely delegated to AI
• Applying human judgment and maintaining accountability in software development
Requirements
Prior experience with coding or AI tools is not required.
A basic understanding of code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is necessary.
Target Audience:
This course is ideal for individuals exploring AI-assisted development, including non-coders, occasional programmers, and 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.
