AI TRAINING
Cursor IDE for AI-Assisted Software Development
Master Cursor's AI features to ship production code faster without sacrificing quality or control.
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Run the diagnostic →What it covers
A hands-on practitioner workshop teaching engineers how to use Cursor's Composer, Agents, and Rules files to accelerate real product development. Participants work through multi-file refactors, codebase-aware completions, and custom agent workflows using their own repositories. The programme covers migration patterns from VS Code and GitHub Copilot, helping teams decide when and why to switch. By the end, participants can configure Cursor for their stack, write effective Rules files, and integrate AI-assisted coding into a team-wide workflow.
What you'll be able to do
- Configure Cursor with a custom Rules file tailored to your team's stack and coding conventions
- Use Composer to perform a multi-file refactor across an existing codebase with minimal manual correction
- Set up and run a Cursor Agent to complete a scoped feature task end-to-end
- Evaluate whether Cursor or GitHub Copilot is the better fit for a given team context and justify the decision
- Integrate AI-assisted coding into a pull-request-based team workflow without introducing review bottlenecks
Topics covered
- Cursor Composer: multi-file edits and iterative generation
- Cursor Agents: autonomous task delegation and codebase traversal
- Rules files: project-level and global configuration for consistent AI behaviour
- Codebase indexing and context window management
- Migration from VS Code: settings, extensions, and keybindings
- Cursor vs GitHub Copilot: capability gaps, cost model, and team fit
- Prompt engineering patterns for code generation and refactoring
- Team workflow integration: code review, branching, and AI diff management
Delivery
Delivered in-person or live-remote (Zoom/Meet with screen sharing). Each participant works on their own laptop with Cursor installed and a personal or team repository loaded. Ratio is approximately 30% instruction and 70% hands-on exercises. Materials include a starter Rules file template library, a prompt pattern cheat sheet, and a VS Code-to-Cursor migration checklist. A two-day format (2 × 6-8 hours) allows deeper project work; a condensed one-day version covers core features only.
What makes it work
- Creating a shared team Rules file before rollout so AI behaviour is consistent across contributors
- Starting with a real in-progress feature rather than toy exercises to anchor learning to actual work
- Establishing a lightweight review norm for AI-generated diffs so quality standards are maintained
- Running a follow-up session two weeks post-workshop to review what stuck and tune the Rules file
Common mistakes
- Using Cursor like a smarter autocomplete rather than leveraging Composer and Agents for multi-step tasks
- Skipping Rules files, leaving AI outputs inconsistent with team conventions and causing review friction
- Over-trusting Agent output on large refactors without scoping tasks into small, verifiable steps
- Migrating the whole team at once without a pilot group to establish shared norms and a baseline Rules file
When NOT to take this
A team whose primary bottleneck is architectural design or product clarity, not coding speed, will see little return; investing in Cursor before solving upstream planning dysfunction is premature.
Providers to consider
Sources
Use cases this training unlocks
- AI-Assisted Code Generation and ReviewAccelerate software delivery by automating code suggestions, boilerplate generation, and PR security reviews.
- AI-Generated Test Cases and UI Regression DetectionAutomatically generate test cases from requirements and catch UI regressions for engineering teams.
- Automated Bug Detection and ClassificationAutomatically detect, classify, and prioritize bugs so engineering teams fix what matters first.
- Intelligent Code Migration AssistantAccelerate codebase migrations between languages, frameworks, or architectures using generative AI.
- Automated Technical Documentation from CodeAutomatically generate and maintain technical documentation from source code and architecture decisions for engineering teams.
- ML-Driven Test Prioritization for CI/CDAutomatically rank and select tests based on code changes to catch defects faster with less compute.
Other trainings at this level
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.