Course Outline
Tool Proficiency and Initial Build
Insights into AI Coding Mechanisms
Key topics include:
\u2022 Comprehending context windows and their inherent limitations
\u2022 Understanding statelessness and how AI models retain session information
\u2022 Mastering the Plan \u2192 Execute \u2192 Review workflow
\u2022 Identifying the strengths and weaknesses of AI coding tools
\u2022 Adopting best practices for effective collaboration with AI assistants<\/p>
The Current AI Coding Ecosystem
Key topics include:
\u2022 An overview of the prevailing AI coding landscape
\u2022 Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
\u2022 Selecting the appropriate model and tool for specific tasks
\u2022 Evaluating the strengths and constraints of various coding assistants
\u2022 Strategic recommendations for adopting tools within development teams<\/p>
Deconstructing Prompts
Key topics include:
\u2022 Identifying the essential components of a high-quality prompt
\u2022 Clearly providing context and defining task objectives
\u2022 Specifying desired output formats and constraints
\u2022 Utilizing common prompting frameworks and templates
\u2022 Techniques for enhancing prompt consistency and quality<\/p>
Initial Coding: Development from Zero
Key topics include:
\u2022 Constructing a project starting from an empty directory
\u2022 Establishing the initial application architecture and scaffolding
\u2022 Managing dependencies and project configurations
\u2022 Iteratively refining generated code
\u2022 Testing and polishing the final solution<\/p>
Existing Codebases, Customization, and Assessment
Integration into Existing Codebases
Key topics include:
\u2022 Navigating and interpreting unfamiliar code structures
\u2022 Querying and analyzing existing projects with AI assistance
\u2022 Mapping application architectures and dependencies
\u2022 Generating documentation and technical summaries
\u2022 Streamlining the onboarding process for new team members<\/p>
Routine Tasks: Bug Fixing, Feature Development, and Testing
Key topics include:
\u2022 Leveraging AI tools to diagnose and resolve bugs
\u2022 Developing new features and system enhancements
\u2022 Creating and optimizing automated tests
\u2022 Verifying the correctness of generated code and changes
\u2022 Boosting productivity in daily development activities<\/p>
Customization: Concepts and Application
Key topics include:
\u2022 Understanding project rules and configuration files
\u2022 Introduction to AGENTS.md and project memory concepts
\u2022 Determining when and where customization mechanisms are effective
\u2022 Best practices for configuring AI assistants
\u2022 Overview of advanced implementation strategies<\/p>
Safeguards, Risks, and Professional Judgment
Key topics include:
\u2022 Reviewing and validating code generated by AI
\u2022 Understanding common failure modes and system limitations
\u2022 Recognizing prompt injection and associated security risks
\u2022 Determining which tasks are suitable for AI delegation
\u2022 Applying human judgment and maintaining accountability in software development<\/p>
Requirements
There are no prerequisites regarding prior coding experience or familiarity with AI tools.
Basic knowledge of code structure or Git is advantageous<\/p>
Access to a licensed account (Claude Code, Cursor, or Copilot)<\/p>
<\/p>
Target Audience:<\/strong><\/p>
This course is ideal for newcomers to AI-assisted development, including non-coders, casual coders, and professionals in technical-adjacent roles such as QA, data analysis, product management, or operations. No prior development background is necessary.<\/p>
Testimonials (2)
Considerations about privacy and policy
Angelo Stefano Soglia - Parkino srl
Course - Claude AI for Workflow Automation and Productivity
Learning how to prompt Claude and use it to digest all of the data I have available.
