Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
• Comprehending context windows and their inherent constraints
• Understanding statelessness and how AI models handle information retention within a session
• The Plan → Execute → Review workflow
• Identifying where AI coding tools excel and where they face limitations
• Best practices for productive collaboration with AI assistants
The AI Coding Landscape
Topics covered:
• An overview of the current AI coding ecosystem
• Differentiating between tools like Cursor, GitHub Copilot, and Claude Code
• Choosing 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
Topics covered:
• Essential components of a high-quality prompt
• Establishing context and clearly defining tasks
• Defining output formats and constraints
• Common prompting frameworks and templates
• Strategies for enhancing prompt quality and consistency
First Coding: Build From Scratch
Topics covered:
• Constructing a project starting from an empty folder
• Setting up the initial application structure and scaffolding
• Handling dependencies and project configuration
• Iteratively refining generated code
• Testing and perfecting the final solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Topics covered:
• Navigating and deciphering unfamiliar codebases
• Querying and analyzing existing projects with AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating the onboarding process for existing projects
Everyday Tasks: Fix, Feature and Test
Topics covered:
• Leveraging AI tools to diagnose and resolve bugs
• Implementing new features and enhancements
• Creating and refining automated tests
• Validating generated code and changes
• Boosting productivity in routine development activities
Personalisation: What It Is
Topics covered:
• 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
Topics covered:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgement and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is necessary.
Basic familiarity with code or Git is advantageous
Licenced Account (Claude Code / Cursor / Copilot)
Audience:
Individuals new to AI-assisted development, including non-coders and occasional coders, as well as technical-adjacent roles in QA, data, product, or operations. No development background 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.