Course Outline
Understanding the Tools and Your First Build
The Mechanics Behind AI Coding Tools
Key topics include:
• Understanding context windows and their inherent limitations
• Exploring statelessness and how AI models manage information within a session
• The Plan → Execute → Review workflow cycle
• Identifying strengths of AI coding tools and recognizing their limitations
• Best practices for effective collaboration with AI assistants
The Current AI Coding Ecosystem
Key topics include:
• An overview of the modern AI coding landscape
• Comparing key tools such as Cursor, GitHub Copilot, and Claude Code
• Strategies for selecting the optimal model and tool for specific tasks
• Analyzing the strengths and constraints of various coding assistants
• Practical guidance for adopting these tools within development teams
Deconstructing Effective Prompts
Key topics include:
• The essential elements of a high-quality prompt
• Techniques for providing clear context and task definitions
• Setting specific output formats and constraints
• Common prompting frameworks and reusable templates
• Methods for enhancing prompt consistency and quality
Initial Coding: Building from the Ground Up
Key topics include:
• Developing a project starting from an empty directory
• Establishing the initial application structure and scaffolding
• Managing project dependencies and configuration files
• Iteratively refining AI-generated code
• Testing and polishing the final solution
Existing Codebases, Customization, and Review
Navigating Existing Codebases
Key topics include:
• Analyzing and understanding unfamiliar code structures
• Using AI tools to query and dissect existing projects
• Mapping out application architecture and dependencies
• Generating technical documentation and summaries
• Streamlining the onboarding process into established projects
Routine Development: Bug Fixes, Features, and Testing
Key topics include:
• Leveraging AI tools to diagnose and resolve bugs
• Implementing new features and system enhancements
• Creating and optimizing automated test suites
• Validating AI-generated changes and code quality
• Boosting productivity in everyday development activities
Personalization: Concepts and Application
Key topics include:
• Understanding project-specific rules and configuration files
• Introduction to AGENTS.md and the concept of project memory
• Identifying when and where personalization features are most effective
• Best practices for configuring AI assistant behavior
• An overview of advanced implementation strategies
Guardrails, Risk Management, and Professional Judgment
Key topics include:
• Reviewing and validating code generated by AI
• Understanding common failure patterns and tool limitations
• Identifying prompt injection techniques and security vulnerabilities
• Determining which tasks are suitable for AI delegation
• Exercising human oversight and maintaining accountability in software delivery
Requirements
No prior experience with coding or AI tools is necessary to participate.
Basic familiarity with code or Git is advantageous
Access to a licensed account (Claude Code / Cursor / Copilot)
Target Audience:
Individuals new to AI-assisted development, including non-developers, casual coders, and professionals in adjacent technical fields such as QA, data, product management, or operations. No prior development experience 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.
