Course Outline
Grasping the Tools and Your First Build
The Inner Workings of AI Coding Tools
Key topics include:
• Understanding context windows and their inherent constraints
• Statelessness and how AI models manage information within a session
• The Plan → Execute → Review cycle
• Identifying strengths and weaknesses in AI coding capabilities
• Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
• An overview of the current AI coding landscape
• Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Strengths and limitations of various coding assistants
• Practical advice for adopting tools within development teams
Anatomy of a Prompt
Key topics include:
• Core elements of high-quality prompts
• Providing context and clearly defining tasks
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Methods for enhancing prompt quality and consistency
Initial Coding: Building from Scratch
Key topics include:
• Starting a project from an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project settings
• Iteratively refining generated code
• Testing and finalizing the solution
Existing Codebases, Personalization, and Review
Working Within a Codebase
Key topics include:
• Navigating and interpreting unfamiliar codebases
• Querying and analyzing existing projects with AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Speeding up the onboarding process for existing projects
Daily Tasks: Fixes, Features, and Testing
Key topics include:
• Leveraging AI tools to investigate and resolve bugs
• Implementing new features and improvements
• Creating and enhancing automated tests
• Verifying generated code and changes
• Boosting productivity in routine development activities
Personalization: Concepts and Application
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and the concept of project memory
• Scenarios where personalization mechanisms are applicable
• Best practices for configuring AI assistants
• An overview of advanced implementation strategies
Guardrails, Risks, and Critical Judgment
Key topics include:
• Reviewing and validating code generated by AI
• Understanding common failure modes and constraints
• Identifying prompt injection threats and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
No previous experience with coding or AI tools is necessary to participate.
Basic familiarity with code or Git is advantageous
A licensed account (for Claude Code, Cursor, or Copilot)
Target Audience:
This course is ideal for those new to AI-assisted development, including non-coders, occasional developers, and professionals in technical-adjacent roles such as QA, data analysis, product management, or operations. No prior development background is expected.
Testimonials (3)
start to use AI with purpose
Alessandro - IC Outsourcing
Course - Claude AI - Fast Track Session
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.
