Course Outline
Grasping the Tools and Executing a First Build
The Mechanics of AI Coding Tools
Key topics include:
• Comprehending context windows and their inherent constraints
• Statelessness and how AI models preserve information throughout a session
• The Plan → Execute → Review operational workflow
• Identifying the strengths and weaknesses of AI coding tools
• Best practices for effective collaboration with AI assistants
The Current AI Coding Ecosystem
Key topics include:
• An overview of the contemporary AI coding landscape
• Differentiating between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Analyzing the capabilities and limitations of various coding assistants
• Strategic advice for adopting tools within development teams
Structure and Design of Effective Prompts
Key topics include:
• Core elements of a high-quality prompt
• Establishing clear context and defining tasks precisely
• Defining output formats and applying constraints
• Utilizing common prompting frameworks and templates
• Strategies for enhancing prompt clarity and consistency
Initial Implementation: Building from the Ground Up
Key topics include:
• Developing a project starting from an empty directory
• Establishing the initial application architecture and scaffolding
• Managing dependencies and configuring project settings
• Refining generated code through iterative improvements
• Testing and perfecting the final solution
Existing Codebases, Customization, and Validation
Navigating Existing Codebases
Key topics include:
• Exploring and understanding unfamiliar code structures
• Utilizing AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating technical documentation and summaries
• Streamlining the onboarding process into established projects
Routine Tasks: Debugging, Feature Development, and Testing
Key topics include:
• Leveraging AI tools to diagnose and resolve bugs
• Developing new features and enhancements
• Writing and optimizing automated tests
• Verifying generated code and implemented changes
• Boosting productivity in daily development activities
Personalization: Core Concepts
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Determining where and when personalization mechanisms are applicable
• Best practices for configuring AI assistants
• An overview of advanced implementation methods
Guardrails, Risks, and Critical Judgment
Key topics include:
• Reviewing and validating AI-generated code
• Understanding common failure patterns and limitations
• Identifying prompt injection and security vulnerabilities
• Determining which tasks are suitable for AI delegation
• Applying human oversight and maintaining accountability in software development
Requirements
No previous experience with coding or AI tools is necessary.
Basic knowledge of code or Git is advantageous
A licensed account (Claude Code / Cursor / Copilot)
Intended Audience:
This course is suitable for newcomers to AI-enhanced development, including non-developers and infrequent coders, as well as 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.
