Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
• Grasping context windows and their inherent limitations
• Statelessness and how AI models retain information within a session
• The Plan → Execute → Review workflow
• Identifying strengths and weaknesses of AI coding tools
• Best practices for effective collaboration with AI assistants
The AI Coding Landscape
Key topics include:
• An overview of the current AI coding ecosystem
• Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for specific tasks
• Analyzing the strengths and limitations of various coding assistants
• Practical advice for adopting tools within development teams
Prompt Anatomy
Key topics include:
• Essential components of a high-quality prompt
• Clearly defining context and task requirements
• Specifying output formats and constraints
• Exploring common prompting frameworks and templates
• Techniques to enhance prompt quality and consistency
First Coding: Build From Scratch
Key topics include:
• Developing a project from an empty directory
• Establishing initial application structure and scaffolding
• Managing dependencies and project configurations
• Iteratively refining generated code
• Testing and polishing the final solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Key topics include:
• Navigating and comprehending unfamiliar codebases
• Utilizing AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding into legacy or existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
• Leveraging AI tools to debug and resolve issues
• Implementing new features and enhancements
• Creating and optimizing automated tests
• Verifying generated code and modifications
• Boosting productivity in routine development tasks
Personalisation: What It Is
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identifying where and when personalization mechanisms are effective
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies
Guardrails, Risks and Judgement
Key topics include:
• Reviewing and validating code generated by AI
• Understanding common failure modes and limitations
• Recognizing prompt injection and security vulnerabilities
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
No prior experience in coding or using AI tools is necessary.
Basic familiarity with code or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot.
Audience:
This course is tailored for individuals new to AI-assisted development, including non-coders, occasional developers, and technical roles in QA, data, product, or operations. No development background is presumed.
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.
