Course Outline
Tool Familiarization and Initial Development
Mechanics of AI Coding Tools
Key topics include:
• Grasp the concept of context windows and their inherent constraints
• Explore statelessness and how AI models handle information retention within a session
• Master the Plan → Execute → Review methodology
• Identify strengths and weaknesses of AI coding tools
• Adopt best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
• Survey the current landscape of AI coding solutions
• Distinguish between platforms like Cursor, GitHub Copilot, and Claude Code
• Select appropriate models and tools based on specific tasks
• Evaluate the advantages and limitations of different coding assistants
• Receive practical advice for integrating tools into development teams
Deconstructing Effective Prompts
Key topics include:
• Identify the essential elements of a high-quality prompt
• Clearly define context and task parameters
• Specify desired output formats and boundaries
• Utilize common prompting frameworks and templates
• Apply techniques to enhance prompt clarity and consistency
Initial Development: Creating from Scratch
Key topics include:
• Construct a project starting from an empty directory
• Establish the initial application architecture and scaffolding
• Manage dependencies and project settings
• Iteratively refine generated code
• Test and polish the final implementation
Existing Codebases, Customization, and Evaluation
Navigating Existing Codebases
Key topics include:
• Explore and comprehend unfamiliar code structures
• Query and analyze existing projects leveraging AI tools
• Map out application architecture and dependencies
• Generate technical documentation and summaries
• Speed up the onboarding process for existing projects
Day-to-Day Operations: Debugging, Features, and Testing
Key topics include:
• Utilize AI tools for bug investigation and resolution
• Develop new features and improvements
• Author and enhance automated tests
• Verify generated code and modifications
• Boost efficiency in routine development activities
Customization: Core Concepts
Key topics include:
• Understand project rules and configuration files
• Introduction to AGENTS.md and the concept of project memory
• Determine where and when to apply customization mechanisms
• Follow best practices for configuring AI assistants
• Review advanced implementation strategies
Safeguards, Risks, and Critical Thinking
Key topics include:
• Scrutinize and validate code generated by AI
• Understand common failure modes and tool limitations
• Identify prompt injection threats and security vulnerabilities
• Determine which tasks are suitable for AI delegation
• Exercise human judgment and maintain accountability in the development process
Requirements
Previous experience with coding or AI tools is not a prerequisite.
Basic knowledge of code or Git is beneficial
A licensed account for one of the following: Claude Code, Cursor, or Copilot
Target Audience:
Individuals new to 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 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.
