Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
• Comprehending context windows and their inherent limitations
• Understanding statelessness and how AI models manage information retention during sessions
• Mastering the Plan → Execute → Review workflow
• Identifying the strengths and weaknesses of AI coding tools
• Adopting best practices for effective collaboration with AI assistants
The AI Coding Landscape
Key topics include:
• An overview of the contemporary 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 constraints of various coding assistants
• Strategic recommendations for integrating these tools into development teams
Prompt Anatomy
Key topics include:
• Identifying the core components of a successful prompt
• Clearly defining tasks and providing necessary context
• Setting specific output formats and constraints
• Utilizing common prompting frameworks and templates
• Strategies for enhancing prompt quality and consistency
First Coding: Build From Scratch
Key topics include:
• Initiating project development from an empty directory
• Establishing initial application structures 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 interpreting unfamiliar codebases
• Utilizing AI tools to query and analyze existing projects
• Mapping application architectures and dependencies
• Generating technical documentation and summaries
• Streamlining the onboarding process for existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
• Leveraging AI tools for bug investigation and resolution
• Developing new features and enhancements
• Creating and optimizing automated tests
• Validating AI-generated code and changes
• Boosting productivity in routine development activities
Personalisation: What It Is
Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Determining where and when to apply personalization mechanisms
• Best practices for configuring AI assistants
• An overview of advanced implementation strategies
Guardrails, Risks and Judgement
Key topics include:
• Reviewing and verifying AI-generated code
• Understanding common failure modes and tool limitations
• Identifying prompt injection and other security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and ensuring 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 one of the following: Claude Code, Cursor, or Copilot
Target Audience:
This course is tailored for individuals new to AI-assisted development, including non-coders, occasional coders, and professionals in technical-adjacent roles such as QA, data analysis, product management, or operations. No prior 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.
