Course Outline
Day 1 | Understanding the Tools and a First Build
Module 1 | How AI Coding Tools Actually Work
Topics covered:
• Understanding context windows and their limitations
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• What AI coding tools can do well and where they struggle
• Best practices for collaborating effectively with AI assistants
Module 2 | The AI Coding Landscape
Topics covered:
• Overview of the current AI coding ecosystem
• Understanding the differences between tools such as Cursor, GitHub Copilot and Claude Code
• Selecting the right model and tool for different tasks
• Strengths and limitations of various coding assistants
• Practical recommendations for tool adoption within development teams
Module 3 | Prompt Anatomy
Topics covered:
• The key components of an effective prompt
• Providing context and defining the task clearly
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for improving prompt quality and consistency
Module 4 | First Coding: Build From Scratch
Topics covered:
• Building a project from an empty folder
• Creating the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively improving the generated code
• Testing and refining the final solution
Day 2 | Existing Codebases, Personalisation and Review
Module 5 | Working in a Codebase
Topics covered:
• Navigating and understanding an unfamiliar codebase
• Querying and analysing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into existing projects
Module 6 | Everyday Tasks: Fix, Feature and Test
Topics covered:
• Using AI tools to investigate and fix bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and changes
• Increasing productivity in day-to-day development tasks
Module 7 | Personalisation: What It Is
Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Where and when personalisation mechanisms apply
• Best practices for configuring AI assistants
• Overview of advanced implementation approaches
Module 8 | Guardrails, Risks and Judgement
Topics covered:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognising prompt injection and security risks
• Deciding what work can be delegated to AI
• Applying human judgement and maintaining accountability in software development
Requirements
No prior coding or experience with AI tools is required.
Familiarity with code or Git is beneficial but not mandatory.
A licensed account for Claude Code, Cursor, or Copilot is needed.
Target Audience:
This course is ideal for those new to AI-assisted development, including non-coders, occasional coders, and technical-adjacent professionals in QA, data, product management, or operations. No prior development background is assumed.
Testimonials (2)
Using Claude Code in a more efficient way
Virgil Trif - Frequentis
Course - Claude Code: Agentic AI Development · 1-Day
"I learned the potential of the tool and gained sufficient skills to start using it for my work right away
