Course Objective & Scope
Many developers either use IDE coding assistants only as glorified autocomplete or accept unverified multi-line suggestions without proper context, accumulating technical debt. This course turns AI coding assistants into disciplined pair-programming partners that speed up delivery without sacrificing code quality.
Participants practise precise context selection (@file, @symbol, @docs), inline edits and multi-file changes in GitHub Copilot, Cursor and Windsurf, immediate verification via type systems and unit tests, project instruction files, and security/licence checks on a realistic codebase.
Who Should Attend?
- Backend, frontend, full-stack and mobile software engineers
- Tech leads and software architects
- Developer experience (DevEx) and platform engineers
- Prerequisite: active coding experience in at least one programming language and Git workflows
Key Takeaways
- Choose the right interaction mode between inline completion, inline edit and multi-file chat/composer
- Pin symbols, type definitions and reference files to eliminate hallucinated APIs
- Verify assistant output through compilers, linters and unit tests before committing
- Codify team conventions in
.github/copilot-instructions.mdor.cursor/rules - Prevent secret leakage, insecure code patterns and hallucinated package risks
Curriculum
How IDE Coding Assistants Work and Mode Selection
- Comparing GitHub Copilot, Cursor, Windsurf and JetBrains AI architectures
- Boundaries between Tab completion, inline edit and multi-file modes
Context Management Inside the IDE
- Pinning open tabs, symbol references, type/interface definitions and documentation
- Steering generation deterministically with signature-first and comment-driven prompts
Everyday Coding Scenarios: Boilerplate, APIs and Data Mapping
- DTO/schema mappings, database queries, client SDKs and error-handling code
- Breaking larger changes into small, reviewable micro-commits
Project Rules and Team Coding Standards
- Encoding architectural rules and style guides in repository instruction files
- Shared prompt templates and reusable team workflows
Quality, Security and Verification Habits
- Catching subtle logic bugs and verifying with a test-first mindset
- Privacy settings, dependency verification and pre-PR self-review checklist
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