Course Outline
Tool Fundamentals and Initial Project Build
Inside the Mechanics of AI Coding Tools
Key topics include:
\u2022 Grasping context windows and their inherent limitations
\u2022 Understanding statelessness and information retention during AI model sessions
\u2022 The Plan \u2192 Execute \u2192 Review workflow
\u2022 Identifying areas where AI coding tools excel versus where they face challenges
\u2022 Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
\u2022 An overview of the current AI coding landscape
\u2022 Differentiating between tools such as Cursor, GitHub Copilot, and Claude Code
\u2022 Selecting appropriate models and tools for specific tasks
\u2022 Evaluating the strengths and constraints of various coding assistants
\u2022 Strategic recommendations for integrating tools into development teams
Anatomy of Effective Prompts
Key topics include:
\u2022 Essential components of a high-quality prompt
\u2022 Providing clear context and task definitions
\u2022 Defining output formats and constraints
\u2022 Common prompting frameworks and reusable templates
\u2022 Techniques for enhancing prompt quality and consistency
Initial Coding: Building from Scratch
Key topics include:
\u2022 Developing a project starting from an empty directory
\u2022 Establishing initial application structure and scaffolding
\u2022 Managing dependencies and project configurations
\u2022 Iteratively refining generated code
\u2022 Testing and polishing the final solution
Navigating Existing Codebases, Personalization, and Review
Working Within Existing Codebases
Key topics include:
\u2022 Navigating and comprehending unfamiliar codebases
\u2022 Querying and analyzing existing projects with AI tools
\u2022 Mapping application structures and dependencies
\u2022 Generating documentation and technical summaries
\u2022 Accelerating the onboarding process for new team members
Daily Tasks: Bug Fixing, Feature Development, and Testing
Key topics include:
\u2022 Utilizing AI tools to investigate and resolve bugs
\u2022 Implementing new features and enhancements
\u2022 Writing and improving automated tests
\u2022 Validating generated code and changes
\u2022 Boosting productivity in routine development tasks
Personalization: Conceptual Overview
Key topics include:
\u2022 Understanding project rules and configuration files
\u2022 Introduction to AGENTS.md and project memory concepts
\u2022 Identifying where and when personalization mechanisms are applicable
\u2022 Best practices for configuring AI assistants
\u2022 Overview of advanced implementation strategies
Guardrails, Risks, and Professional Judgment
Key topics include:
\u2022 Reviewing and validating AI-generated code
\u2022 Understanding common failure modes and limitations
\u2022 Recognizing prompt injection and security risks
\u2022 Determining which tasks are suitable for AI delegation
\u2022 Applying human judgment and maintaining accountability in software development
Requirements
No prior coding or experience with AI tools is necessary.
Basic familiarity with code or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot.
Target Audience:
Individuals new to AI-assisted development, including non-coders, occasional programmers, and technical-adjacent professionals in QA, data, product, or operations roles. No prior development background is assumed.
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.
