Course Outline
Understanding the Tools and Initial Build
How AI Coding Tools Function
Key topics include:
• Context windows and their inherent limitations
• Statelessness and how AI models maintain information throughout a session
• The Plan → Execute → Review workflow
• Capabilities and challenges of AI coding tools
• Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
• An overview of the current AI coding landscape
• Distinctions between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams
Deconstructing Prompts
Key topics include:
• Essential elements of effective prompting
• Providing clear context and 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:
• Developing a project starting from an empty directory
• Establishing initial application structure and scaffolding
• Managing dependencies and project settings
• Iteratively refining generated code
• Testing and polishing the final solution
Existing Codebases, Customization, and Review
Navigating Existing Codebases
Key topics include:
• Exploring and comprehending unfamiliar codebases
• Querying and analyzing existing projects with AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding for existing projects
Routine Tasks: Debugging, Features, and Testing
Key topics include:
• Leveraging AI tools to identify and resolve bugs
• Developing new features and enhancements
• Creating and improving automated tests
• Verifying generated code and modifications
• Boosting productivity in daily development tasks
Customization: An Overview
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Application of personalization mechanisms in different contexts
• 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
• Identifying prompt injection and security risks
• Determining which tasks can be delegated to AI
• Applying human judgment and maintaining accountability in software development
Requirements
No previous experience with coding or AI tools is necessary.
A basic understanding of code or Git is advantageous
A licensed account (Claude Code / Cursor / Copilot)
Target Audience:
Individuals new to AI-assisted development, ranging from non-coders and occasional developers to technical-adjacent roles in 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.