Course Outline
Day 1 | Understanding the Tools and Initial Project Creation
Module 1 | How AI Coding Tools Function
Topics covered:
• Grasp of context windows and their inherent constraints
• The concept of statelessness and how AI models maintain information during a session
• The Plan → Execute → Review operational workflow
• Strengths and weaknesses of AI coding tools
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Ecosystem
Topics covered:
• Overview of the current landscape of AI coding solutions
• Key differences between tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for specific tasks
† Strengths and limitations of various coding assistants
• Practical strategies for adopting tools within development teams
Module 3 | Components of Effective Prompts
Topics covered:
• Essential elements of a well-crafted prompt
• Providing clear context and defining the task explicitly
• Specifying output formats and necessary constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Module 4 | Initial Coding: Project Creation from Scratch
Topics covered:
• Setting up a project in an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining the generated code
• Testing and optimizing the final solution
Day 2 | Handling Existing Codebases, Customization, and Review
Module 5 | Working Within a Codebase
Topics covered:
• Navigating and comprehending unfamiliar code structures
• Using AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Accelerating the onboarding process for new team members
Module 6 | Routine Tasks: Bug Fixes, Features, and Testing
Topics covered:
• Utilizing 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 | Customization: Core Concepts
Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Applicability of customization mechanisms
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies
Module 8 | Guardrails, Risks, and Judgment
Topics covered:
• Reviewing and validating code generated by AI
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is required.
Familiarity with basic code concepts or Git is beneficial.
A valid license account for Claude Code, Cursor, or Copilot is needed.
Audience:
This course is ideal for beginners in AI-assisted development, including non-programmers, 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
