Course Outline
Day 1 | Understanding the Tools and a First Build
Module 1 | How AI Coding Tools Actually Work
Topics covered:
• Comprehending context windows and their inherent limitations
• Grasping statelessness and how AI models retain information during a session
• Exploring the Plan → Execute → Review workflow
• Identifying where AI coding tools excel and where they fall short
• Adopting best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Landscape
Topics covered:
• A comprehensive overview of the current AI coding ecosystem
• Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Recognising the strengths and limitations of various coding assistants
• Practical advice on integrating these tools into development teams
Module 3 | Prompt Anatomy
Topics covered:
• The essential components of an effective prompt
• Providing clear context and defining the task precisely
• Specifying output formats and constraints
• Familiarity with common prompting frameworks and templates
• Techniques for enhancing the quality and consistency of prompts
Module 4 | First Coding: Build From Scratch
Topics covered:
• Constructing a project from an empty directory
• Establishing the 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 comprehending an unfamiliar codebase
• Querying and analysing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating the onboarding process for existing projects
Module 6 | Everyday Tasks: Fix, Feature and Test
Topics covered:
• Leveraging AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and modifications
• Boosting productivity in daily development activities
Module 7 | 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 apply
• Best practices for configuring AI assistants
• An 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
• Determining which tasks can be safely delegated to AI
• Applying human judgement and maintaining accountability in software development
Requirements
No prior coding knowledge or experience with AI tools is necessary.
A basic understanding of code or Git is advantageous.
Requires an active licensed account for Claude Code, Cursor, or Copilot.
Target Audience:
This course is ideal for individuals new to AI-assisted development, including non-programmers, occasional coders, and technical-adjacent professionals in QA, data analytics, 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
