Cursor for AI-Native Development Training

Learn via : Virtual Classroom / Online
Duration : 2 Days
Download PDF
  1. Home
  3. Cursor for AI-Native Development Training

Description

The purpose of this training is to enable software development teams to use Cursor not merely as a code completion tool, but as an AI development assistant capable of understanding and working at the project level.

By the end of the training, participants will be able to effectively use Agent Mode, safely perform refactoring across multi-file codebases, accelerate testing and debugging processes, and establish a structured Cursor usage model at the enterprise level.

Key Outcomes of the Training

  • Establishing enterprise-level Cursor usage standards
  • Increasing developer productivity
  • Accelerating testing and refactoring processes
  • Achieving systematic improvements in code quality
  • Reducing risks associated with AI-assisted development

Outline

Day 1 – Cursor Fundamentals and Daily Development Practices

1. What is AI-Native Development?

  • Traditional IDE vs. AI-first IDE approach
  • AI-driven coding model
  • Prompt → Plan → Code → Test development cycle
  • The role of Cursor in the software development workflow

2. Cursor Interface and Core Features

  • Using the chat panel
  • Inline code generation and editing
  • Code explanation and bug fixing
  • Diff analysis and change management

3. Agent Mode and Context Management

  • What is Agent Mode?
  • Project-level analysis approach
  • Multi-file refactoring workflows
  • File selection and context control
  • Optimizing token usage

4. Daily Usage Scenarios

  • Developing new features
  • Understanding existing codebases
  • Generating unit tests
  • Creating code documentation
  • Applying refactoring techniques
  • MCP integration

Day 2 – Advanced Cursor Usage and Enterprise Adoption

5. Using Cursor in Large Codebases

  • Monorepo scenarios
  • Managing context limitations
  • Breaking down high-risk changes into smaller tasks
  • Refactoring strategies for large projects

6. Testing, Debugging, and Code Quality

  • AI-assisted unit test generation
  • Edge-case analysis
  • Performance optimization
  • Clean code principles

7. Security and Enterprise Usage

  • Validating AI-generated code
  • Analyzing potential security vulnerabilities
  • Using AI tools in regulated industries
  • On-premise vs. cloud model strategies

8. Team-Based Cursor Usage Model

  • Drafting a Cursor usage policy
  • Creating prompt templates
  • Integrating AI into code review processes
  • AI-assisted sprint planning

9. Final End-to-End Scenario

  • Requirement analysis
  • Technical design
  • Code generation
  • MCP integration
  • Writing tests
  • Refactoring and security validation
  • Documentation generation