Course Outline
Grasping the Tools and Initial Construction
The Inner Workings of AI Coding Utilities
Key areas covered:
• Context windows and their inherent constraints
• Statelessness and information retention during sessions
• The Plan → Execute → Review process
• Capabilities and limitations of AI coding tools
• Effective collaboration strategies with AI assistants
The Current AI Coding Environment
Key areas covered:
• An overview of the present AI coding ecosystem
• Distinctions between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Advantages and restrictions of various coding assistants
• Strategic advice for adopting tools within development teams
Anatomy of a Prompt
Key areas covered:
• Essential elements of a high-quality prompt
• Establishing context and clearly defining objectives
• Defining output formats and boundaries
• Standard prompting frameworks and templates
• Methods for enhancing prompt accuracy and consistency
Initial Coding: Developing from Zero
Key areas covered:
• Constructing a project from an empty directory
• Setting up initial application architecture and scaffolding
• Handling dependencies and project settings
• Refining generated code through iterative cycles
• Testing and polishing the final solution
Existing Codebases, Customization, and Evaluation
Working Within a Codebase
Key areas covered:
• Navigating and comprehending unfamiliar code structures
• Using AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical overviews
• Speeding up the onboarding process for existing projects
Routine Tasks: Debugging, Features, and Testing
Key areas covered:
• Employing AI tools to diagnose and resolve bugs
• Developing new features and improvements
• Creating and enhancing automated tests
• Verifying generated code and modifications
• Boosting productivity in daily development activities
Personalization: Concepts and Application
Key areas covered:
• Comprehending project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Applicable contexts for personalization mechanisms
• Best practices for setting up AI assistants
• Overview of sophisticated implementation strategies
Safeguards, Risks, and Decision-Making
Key areas covered:
• Assessing and verifying AI-generated code
• Identifying common failure modes and limitations
• Detecting prompt injection and security threats
• Determining which tasks can be delegated to AI
• Exercising 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 for Claude Code, Cursor, or Copilot.
Intended Audience:
This course suits individuals new to AI-assisted development, including non-coders, occasional programmers, and professionals in adjacent technical fields 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.
