Course Outline
Day 1 | Understanding the Tools and Initial Project Creation
Module 1 | How AI Coding Tools Function
Covered topics:
• Comprehending context windows and their constraints
• The concept of statelessness and how AI models retain information within a session
• The Plan → Execute → Review workflow
• Areas where AI coding tools excel and areas where they face challenges
• Best practices for effective collaboration with AI assistants
Module 2 | Overview of the AI Coding Ecosystem
Covered topics:
• General landscape of current AI coding ecosystems
• Key distinctions between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Strengths and limitations of various coding assistants
• Practical guidelines for adopting these tools within development teams
Module 3 | Structure of Effective Prompts
Covered topics:
• Essential components of a well-crafted prompt
• How to provide clear context and define the task explicitly
• Defining output formats and constraints
• Common prompting frameworks and templates
• Strategies for enhancing prompt quality and consistency
Module 4 | Initial Coding: Building from Scratch
Covered topics:
‡ Creating a project starting from an empty directory
‡ Establishing the initial application structure and scaffolding
‡ Managing dependencies and project configuration
‡ Iteratively refining generated code
‡ Testing and optimizing the final solution
Day 2 | Working with Existing Codebases, Personalization, and Review
Module 5 | Navigating an Existing Codebase
Covered topics:
‡ Understanding and navigating unfamiliar code structures
‡ Using AI tools to query and analyze existing projects
‡ Mapping application structure and dependencies
‡ Generating documentation and technical summaries
‡ Accelerating the onboarding process for new team members
Module 6 | Everyday Tasks: Bug Fixes, Features, and Testing
Covered topics:
‡ Using AI tools to investigate and resolve bugs
‡ Implementing new features and enhancements
‡ Writing and refining automated tests
‡ Validating generated code and proposed changes
‡ Boosting productivity in daily development activities
Module 7 | Understanding Personalization
Covered topics:
‡ Grasping project rules and configuration files
‡ Introduction to AGENTS.md and project memory concepts
‡ Identifying where and when personalization mechanisms apply
‡ Best practices for configuring AI assistants
‡ Overview of advanced implementation strategies
Module 8 | Guardrails, Risks, and Judgment
Covered topics:
‡ Reviewing and validating AI-generated code
‡ Recognizing common failure modes and limitations
‡ Identifying prompt injection and security risks
‡ Determining which tasks can be delegated to AI
‡ Applying human judgment and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is necessary.
Familiarity with basic coding concepts or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
This course is ideal for newcomers to AI-assisted development, including non-programmers, occasional coders, and professionals in technical-adjacent fields such as QA, data analysis, 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
