Course Outline
Tool Comprehension and Initial Construction
Mechanics of AI Coding Tools
Key topics include:
• Grasping context windows and their inherent constraints
• Understanding statelessness and how AI models handle information retention during sessions
• Implementing the Plan → Execute → Review workflow
• Identifying strengths and limitations of AI coding tools
• Best practices for productive collaboration with AI assistants
The Current AI Coding Ecosystem
Key topics include:
• An overview of the contemporary AI coding landscape
• Distinguishing features among tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for specific tasks
• Evaluating the strengths and weaknesses of various coding assistants
• Strategic recommendations for integrating tools into development teams
Structure and Craft of Prompts
Key topics include:
• Essential components of high-impact prompts
• Contextualization and clear task definition
• Defining output formats and constraints
• Utilizing common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Initial Implementation: Building from Scratch
Key topics include:
• Constructing a project starting from an empty directory
• Establishing initial application architecture and scaffolding
• Managing dependencies and project settings
• Iteratively refining generated code
• Testing and finalizing the solution
Existing Codebases, Personalization, and Evaluation
Navigating Existing Codebases
Key topics include:
• Interpreting and exploring unfamiliar code structures
• Leveraging AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical overviews
• Streamlining onboarding processes for established projects
Routine Development: Debugging, Features, and Testing
Key topics include:
• Utilizing AI tools to diagnose and resolve bugs
• Developing new features and enhancements
• Creating and improving automated tests
• Validating AI-generated code and modifications
• Boosting productivity in daily development activities
Personalization: Concepts and Application
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Determining where and when personalization mechanisms are effective
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies
Guardrails, Risks, and Critical Judgment
Key topics include:
• Rigorous review and validation of AI-generated code
• Recognizing common failure modes and limitations
• Identifying prompt injection and security vulnerabilities
• Determining appropriate work for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is necessary.
Basic familiarity with code structures or Git is advantageous.
A licensed account for one of the following: Claude Code, Cursor, or Copilot.
Target Audience:
Professionals new to AI-assisted development, including non-coders, occasional developers, and technical-adjacent roles in QA, data, product, or operations. No previous 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.
