Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
\u2022 Understanding context windows and their constraints
\u2022 Statelessness and how AI models handle information during a session
\u2022 The Plan \u2192 Execute \u2192 Review workflow
\u2022 Capabilities and limitations of AI coding tools
\u2022 Best practices for effective collaboration with AI assistants
The AI Coding Landscape
Key topics include:
\u2022 Overview of the current AI coding ecosystem
\u2022 Understanding differences between tools such as Cursor, GitHub Copilot, and Claude Code
\u2022 Selecting the appropriate model and tool for specific tasks
\u2022 Strengths and limitations of various coding assistants
\u2022 Practical recommendations for adopting tools within development teams
Prompt Anatomy
Key topics include:
\u2022 Essential components of an effective prompt
\u2022 Providing context and clearly defining tasks
\u2022 Specifying 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 Building a project from an empty folder
\u2022 Creating initial application structure and scaffolding
\u2022 Managing dependencies and project configuration
\u2022 Iteratively refining generated code
\u2022 Testing and finalizing the solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Key topics include:
\u2022 Navigating and understanding unfamiliar codebases
\u2022 Querying and analysing existing projects using AI tools
\u2022 Mapping application structure and dependencies
\u2022 Generating documentation and technical summaries
\u2022 Accelerating onboarding into existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
\u2022 Using AI tools to investigate and resolve bugs
\u2022 Implementing new features and enhancements
\u2022 Writing and improving automated tests
\u2022 Validating generated code and changes
\u2022 Boosting productivity in daily 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 Where and when personalisation mechanisms are applied
\u2022 Best practices for configuring AI assistants
\u2022 Overview of advanced implementation approaches
Guardrails, Risks and Judgement
Key topics include:
\u2022 Reviewing and validating AI-generated code
\u2022 Understanding common failure modes and limitations
\u2022 Recognising prompt injection and security risks
\u2022 Determining which tasks can be delegated to AI
\u2022 Applying human judgement and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is necessary.
A basic understanding of code or Git is beneficial.
A licensed account for one of the supported tools (e.g., Claude Code, Cursor, or Copilot).
Target Audience:
This course is suitable for those new to AI-assisted development, including non-developers, occasional coders, and professionals in technical-adjacent roles within QA, data, product, or operations. No previous development background is required.
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.
