Course Outline
Day 1 | Understanding the Tools and a First Build
Module 1 | How AI Coding Tools Actually Work
Topics covered:
• Grasping context windows and their constraints
• Statelessness and how AI models retain information within a session
• The Plan → Execute → Review workflow
• Capabilities and limitations of AI coding tools
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Landscape
Topics covered:
• Overview of the current AI coding ecosystem
• Differences between tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for various tasks
• Strengths and limitations of different coding assistants
• Practical recommendations for adopting tools within development teams
Module 3 | Prompt Anatomy
Topics covered:
• Key components of an effective prompt
• Providing context and clearly defining the task
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Module 4 | First Coding: Build From Scratch
Topics covered:
• Building a project from an empty directory
• Creating initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and polishing the final solution
Day 2 | Existing Codebases, Personalisation and Review
Module 5 | Working in a Codebase
Topics covered:
• Navigating and understanding unfamiliar codebases
• 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 resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and changes
• Increasing productivity in daily 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 AI-tool experience is necessary.
Familiarity with code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
The course is ideal for those new to AI-assisted development, including non-coders, occasional programmers, and technical-adjacent professionals in QA, data, product, or operations roles. 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
