Course Outline
Day 1 | Comprehending the Tools and Initial Build
Module 1 | How AI Coding Tools Operate
Topics covered:
• Understanding context windows and their inherent constraints
• The concept of 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 Ecosystem
Topics covered:
• Overview of the current landscape of AI coding solutions
• Key differences between tools like 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 integrating tools into development teams
Module 3 | Anatomy of a Prompt
Topics covered:
• Essential components of an effective prompt
• Providing adequate 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 | Initial Coding: Building From Scratch
Topics covered:
• Constructing a project starting with an empty directory
• Creating the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and polishing the final solution
Day 2 | Working with Existing Codebases, Personalization, and Review
Module 5 | Navigating a Codebase
Topics covered:
• Navigating and comprehending an unfamiliar codebase
• Querying and analyzing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into ongoing projects
Module 6 | Daily Tasks: Debugging, Features, and Testing
Topics covered:
• Using 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 routine development tasks
Module 7 | Personalization: Foundations
Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Where and when personalization mechanisms are applied
• Best practices for configuring AI assistants
• Overview of advanced implementation approaches
Module 8 | Guardrails, Risks, and Judgment
Topics covered:
• 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 previous experience with coding or AI tools is necessary.
Familiarity with basic coding concepts or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
This course is ideal for beginners in AI-assisted development, including non-programmers and occasional coders, as well as 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
