Course Outline
Comprehending the Tools and Completing a First Build
The Mechanics of AI Coding Tools
Key topics include:
• Grasping context windows and their inherent limits
• Statelessness and the retention of information by AI models during sessions
• The Plan → Execute → Review workflow
• Identifying where AI coding tools excel and where they face challenges
• Best practices for effective collaboration with AI assistants
The AI Coding Ecosystem
Key topics include:
• An overview of the current AI coding landscape
• Distinguishing between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the optimal model and tool for specific tasks
• Analyzing the strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams
Anatomy of Effective Prompts
Key topics include:
• Essential elements of a high-quality prompt
• Providing clear context and defining the task precisely
• Specifying output formats and constraints
• Standard prompting frameworks and templates
• Strategies for enhancing prompt quality and consistency
Initial Coding: Building from Scratch
Key topics include:
• Constructing a project starting from an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project settings
• Iteratively refining generated code
• Testing and polishing the final solution
Existing Codebases, Personalization, and Review
Operating Within a Codebase
Key topics include:
• Navigating and comprehending unfamiliar codebases
• Querying and analyzing existing projects using AI tools
• Mapping application structures and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding into existing projects
Routine Tasks: Debugging, Features, and Testing
Key topics include:
• Utilizing AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Creating and refining automated tests
• Verifying generated code and modifications
• Boosting productivity in daily development tasks
Personalization: Concepts and Application
Key topics include:
• Understanding project rules and configuration files
• An introduction to AGENTS.md and project memory concepts
• Identifying where and when personalization mechanisms are applicable
• Best practices for configuring AI assistants
• An overview of advanced implementation methods
Guardrails, Risks, and Professional Judgment
Key topics include:
• Reviewing and validating code generated 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
No previous coding or AI tool experience is necessary.
Basic knowledge of code or Git is beneficial
A licensed account for Claude Code, Cursor, or Copilot
Audience:
Individuals new to AI-assisted development, such as non-coders, occasional developers, and professionals in technical-adjacent roles like QA, data, product, or operations. No prior development background is required.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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.
