Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include: • Grasping context windows and their inherent constraints • Exploring statelessness and how AI models maintain session information • The Plan → Execute → Review operational 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 • Differentiating tools like Cursor, GitHub Copilot, and Claude Code • Selecting appropriate models and tools for specific tasks • Evaluating the pros and cons of various coding assistants • Practical advice for adopting tools within development teams
Prompt Anatomy
Key topics include: • Essential elements of high-quality prompts • Clearly defining context and task requirements • Specifying desired output formats and constraints • Utilizing common prompting frameworks and templates • Strategies for enhancing prompt consistency and effectiveness
First Coding: Build From Scratch
Key topics include: • Initiating a project from an empty directory • Establishing initial application structure and scaffolding • Managing project configuration and dependencies • Iteratively refining AI-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 • Analyzing existing projects with AI tools • Mapping application architecture 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 for bug investigation and resolution • Developing new features and enhancements • Authoring and optimizing automated tests • Validating code changes and generated output • 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 • Application scenarios for personalisation mechanisms • Best practices for configuring AI assistants • Overview of advanced implementation strategies
Guardrails, Risks and Judgement
Key topics include: • Reviewing and validating AI-generated code • Understanding common failure modes and limitations • Identifying prompt injection and security threats • Determining which tasks are suitable for AI delegation • Applying human judgment and 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)
Audience:
Individuals new to AI-assisted development, including non-coders and occasional coders, as well as professionals in technical-adjacent roles within 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.
