Course Outline
Day 1 | Understanding the Tools and Initial Implementation
Module 1 | How AI Coding Tools Function
Coverage:
• Comprehending context windows and their constraints
• The nature 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 Landscape of AI Coding Solutions
Coverage:
• Overview of the current AI coding ecosystem
• Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Strengths and limitations of various coding assistants
• Practical advice on adopting these tools within development teams
Module 3 | Components of Effective Prompts
Coverage:
• Essential elements of a successful prompt
• Providing clear context and defining the task precisely
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing the quality and consistency of prompts
Module 4 | Initial Coding: Building from Scratch
Coverage:
• Developing a project starting with 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 | Working with Existing Codebases, Personalization, and Review
Module 5 | Navigating a Codebase
Coverage:
• Exploring and understanding unfamiliar code structures
• Querying and analyzing existing projects using AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into ongoing projects
Module 6 | Daily Tasks: Bug Fixes, Features, and Testing
Coverage:
• 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 everyday development activities
Module 7 | Personalization: Core Concepts
Coverage:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory mechanisms
• Applicable scenarios for personalization settings
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies
Module 8 | Guardrails, Risks, and Professional Judgment
Coverage:
• Reviewing and validating code produced by AI
• Understanding common failure modes and limitations
• Identifying prompt injection and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
There are no requirements for prior coding or AI-tool experience.
Familiarity with code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
The course is intended for those new to AI-assisted development, including non-coders, occasional programmers, and technical roles in QA, data, product management, or operations. No prior development background is expected.
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
