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
• Exploring statelessness and how AI models retain information throughout a session
• Mastering the Plan → Execute → Review workflow
• Identifying areas where AI coding tools excel and where they face challenges
• Adopting 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 such as Cursor, GitHub Copilot, and Claude Code
• Selecting the most suitable model and tool for specific tasks
• Evaluating the strengths and limitations of various coding assistants
• Practical strategies for adopting tools within development teams
Prompt Anatomy
Key topics include:
• Identifying the essential components of an effective prompt
• Providing clear context and task definitions
• Specifying output formats and constraints
• Utilizing common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
First Coding: Build From Scratch
Key topics include:
• Initiating a project from an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• 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
• Using AI tools to query and analyse existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding into existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
• Leveraging AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Authoring and improving automated tests
• Validating generated code and changes
• 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 personalisation mechanisms are applicable
• Best practices for configuring AI assistants
• An overview of advanced implementation approaches
Guardrails, Risks and Judgement
Key topics include:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognising prompt injection and security risks
• Determining which tasks can be safely delegated to AI
• Applying human judgement and maintaining accountability in software development
Requirements
There are no prerequisites for coding or AI tool experience.
A basic understanding of code or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot is required.
Audience:
Designed for newcomers to AI-assisted development, this course accommodates non-coders, occasional developers, and technical-adjacent roles in QA, data, product, 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.
