Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
\u2022 Grasping context windows and their inherent limits
\u2022 Understanding statelessness and how AI models handle information across a session
\u2022 The Plan \u2192 Execute \u2192 Review workflow
\u2022 Identifying the strengths and challenges of AI coding tools
\u2022 Best practices for productive collaboration with AI assistants
The AI Coding Landscape
Key topics include:
\u2022 An overview of the current AI coding ecosystem
\u2022 Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
\u2022 Selecting the appropriate model and tool for specific tasks
\u2022 Analyzing the capabilities and constraints of different coding assistants
\u2022 Practical advice for adopting tools within development teams
Prompt Anatomy
Key topics include:
\u2022 Essential elements of a high-quality prompt
\u2022 Providing clear context and defining the task
\u2022 Specifying desired output formats and constraints
\u2022 Common prompting frameworks and templates
\u2022 Techniques for enhancing prompt quality and consistency
First Coding: Build From Scratch
Key topics include:
\u2022 Initiating a project from an empty directory
\u2022 Setting up the initial application structure and scaffolding
\u2022 Managing dependencies and project configurations
\u2022 Iteratively refining generated code
\u2022 Testing and perfecting the final solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Key topics include:
\u2022 Navigating and comprehending unfamiliar codebases
\u2022 Using AI tools to query and analyze existing projects
\u2022 Mapping application architecture and dependencies
\u2022 Generating documentation and technical summaries
\u2022 Accelerating the onboarding process for existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
\u2022 Leveraging AI tools for bug investigation and resolution
\u2022 Implementing new features and enhancements
\u2022 Creating and improving automated tests
\u2022 Verifying generated code and changes
\u2022 Boosting productivity in routine development tasks
Personalisation: What It Is
Key topics include:
\u2022 Understanding project rules and configuration files
\u2022 Introduction to AGENTS.md and project memory concepts
\u2022 Identifying where and when personalization mechanisms are applicable
\u2022 Best practices for configuring AI assistants
\u2022 An overview of advanced implementation strategies
Guardrails, Risks and Judgement
Key topics include:
\u2022 Reviewing and validating AI-generated code
\u2022 Understanding common failure modes and limitations
\u2022 Recognizing prompt injection and security vulnerabilities
\u2022 Determining which tasks can be safely delegated to AI
\u2022 Applying human judgment and maintaining accountability in software development
Requirements
No previous experience with coding or AI tools is necessary.
Basic knowledge of code or Git is beneficial
A licensed account for Claude Code, Cursor, or Copilot
Target Audience:
This course is suitable for newcomers to AI-assisted development, including non-developers and occasional coders, as well as professionals in technical-adjacent roles such as QA, data analysis, product management, or operations. No prior 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.
