Course Outline
Grasping the Tools and Completing a First Build
Understanding the Mechanics of AI Coding Tools
Key topics include:
\u2022 Understanding context windows and their inherent limitations
\u2022 Statelessness and how AI models handle information retention within a session
\u2022 The Plan \u2192 Execute \u2192 Review workflow
\u2022 Identifying strengths and weaknesses in AI coding capabilities
\u2022 Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
\u2022 An overview of the current landscape of AI coding tools
\u2022 Distinguishing between platforms like Cursor, GitHub Copilot, and Claude Code
\u2022 Choosing the appropriate model and tool for specific tasks
\u2022 Evaluating the pros and cons of various coding assistants
\u2022 Practical strategies for adopting tools within development teams
Anatomy of Effective Prompts
Key topics include:
\u2022 Essential elements that make a prompt effective
\u2022 Providing clear context and defining tasks precisely
\u2022 Specifying desired output formats and constraints
\u2022 Utilizing common prompting frameworks and templates
\u2022 Techniques to enhance prompt quality and consistency
Initial Coding: Building from Scratch
Key topics include:
\u2022 Constructing a project starting from an empty folder
\u2022 Establishing the initial application architecture and scaffolding
\u2022 Managing dependencies and project settings
\u2022 Iteratively refining generated code
\u2022 Testing and polishing the final solution
Existing Codebases, Customization, and Review Processes
Operating Within Existing Codebases
Key topics include:
\u2022 Navigating and comprehending unfamiliar code structures
\u2022 Querying and analyzing existing projects with AI tools
\u2022 Mapping application architecture and dependencies
\u2022 Generating documentation and technical summaries
\u2022 Streamlining the onboarding process for existing projects
Routine Tasks: Debugging, Feature Development, and Testing
Key topics include:
\u2022 Leveraging AI tools to diagnose and resolve bugs
\u2022 Implementing new features and enhancements
\u2022 Creating and optimizing automated tests
\u2022 Verifying generated code and changes
\u2022 Boosting productivity in daily development activities
Customization: Core Concepts
Key topics include:
\u2022 Understanding project rules and configuration files
\u2022 Introduction to AGENTS.md and the concept of project memory
\u2022 Identifying when and where customization mechanisms apply
\u2022 Best practices for configuring AI assistants
\u2022 Overview of advanced implementation strategies
Guardrails, Risks, and Human Judgment
Key topics include:
\u2022 Reviewing and validating code generated by AI
\u2022 Understanding common failure modes and limitations
\u2022 Recognizing prompt injection and security threats
\u2022 Determining which tasks are suitable for AI delegation
\u2022 Applying human judgment and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is necessary.
Basic familiarity with code or Git is beneficial
A licensed account (Claude Code / Cursor / Copilot)
Target Audience:
Individuals new to AI-assisted development, including non-coders, occasional coders, and professionals in technical-adjacent roles such as QA, data, product, or operations. No previous development background is assumed.
Testimonials (1)
A key strength of the training was its practical approach, which enabled a clear understanding of how to concretely apply AI tools in day-to-day work activities.
Sabrina Contessa - Parkino srl
Course - Claude AI for Workflow Automation and Productivity
Machine Translated
