I spent six months switching between Cursor and GitHub Copilot. Made commits with both. Shipped features with both. Got frustrated with both.
Everyone wants to know which one is better. Wrong question.
The right question: how do you stop either tool from generating garbage?
The Problem Nobody Talks About
Both Cursor and Copilot are impressive. They autocomplete like magic when you're writing boilerplate. They can explain code snippets. They make you feel productive.
Then you ask them about your actual codebase and they hallucinate. Hard.
I asked Copilot to refactor a payment processing function. It suggested using a class that doesn't exist. When I asked Cursor to update our authentication middleware, it referenced a permissions system we deprecated eight months ago.
These tools are trained on public GitHub repos. They're brilliant at React hooks and Python list comprehensions. But your codebase? Your team's weird naming conventions? That internal API you built last quarter? They know nothing.
The Context Problem
Here's what happened when I tried to use Cursor to add a new feature to our notifications system:
Me: "Add support for email notifications in the notification service"
Cursor: Generated code using sendEmail() and EmailTemplate classes.
Reality: We use NotificationDispatcher.dispatch() with channel-specific handlers. Email templates live in a separate service. None of Cursor's suggestions would compile.
This isn't Cursor being bad. It's Cursor being ignorant. Same thing happens with Copilot.
The AI doesn't know:
What services exist in your codebase
How they're supposed to interact
What patterns your team actually uses
Which code is legacy vs current
Who owns what components
Without this context, you get syntactically correct code that's architecturally wrong.
How I Actually Got Productive
I changed my approach. Instead of treating these tools as magic code generators, I started treating them as really fast typists who need explicit instructions.
The Before: Vague Prompts
// Me: "Add caching to this function"
// Copilot: *suggests using a Map*
Our caching layer uses Redis with specific TTL patterns and key prefixes. The Map suggestion is useless.
The After: Context-Rich Prompts
// Use CacheService.get/set with 'user:profile:' prefix
// TTL: 5 minutes, invalidate on user.update event
// See: src/services/cache/CacheService.ts for examples
async function getUserProfile(userId: string) {
// Now Copilot suggests the right pattern
}
The difference? I told it what exists and how it's used.
The Three Context Layers That Matter
After months of experimentation, I figured out what context actually moves the needle:
1. Architectural Context
Your codebase has structure. Services talk to each other in specific ways. There are layers, boundaries, patterns.
I started keeping an architecture doc open in a second window. When writing code, I'd reference it:
// This service handles webhook processing
// Depends on: EventBus (src/events/), QueueService (src/queue/)
// Publishes: 'webhook.received', 'webhook.processed'
// See: docs/architecture/webhook-flow.md
Suddenly both Cursor and Copilot stopped suggesting random HTTP calls and started following our event-driven patterns.
2. Ownership Context
Who owns this code? Who's maintaining that service?
This matters more than you'd think. When the AI suggests modifying a shared service, you need to know if that's your call to make or if you need to coordinate with another team.
I started annotating files:
/**
* Auth middleware - owned by @platform-team
* Don't modify directly - use extension points
* Contact: #platform-services
*/
The AI still can't read this perfectly, but I can. It helps me catch suggestions that would create cross-team problems.
3. Health Context
Some code is fresh. Some is rotting. The AI can't tell the difference.
I learned to mark code that's deprecated or problematic:
// DEPRECATED: Use AuthServiceV2 instead
// This version has session handling bugs
// Migration guide: docs/auth-v2-migration.md
When Copilot suggests patterns from old code, I know to reject them.
Where Glue Changed Everything
Here's where I'll be honest: manually maintaining all that context is exhausting.
I was spending 30 minutes a day updating comments, documenting patterns, tracking ownership. It helped, but it didn't scale.
Then I tried Glue. It indexes your entire codebase and extracts this context automatically. Maps out services. Tracks who owns what. Identifies high-churn, high-complexity areas.
The killer feature: it integrates directly with Cursor through MCP (Model Context Protocol). When I'm coding, Cursor can query Glue for context about any part of the codebase.
Now when I ask Cursor to modify something, it knows:
What services exist and how they interact
Which code is actively maintained vs legacy
Who owns the components I'm touching
What patterns are actually used in production
Example: I asked Cursor to add rate limiting to an API endpoint. Instead of suggesting a generic Express middleware, it:
Found our existing RateLimitService
Saw how other endpoints use it
Generated code matching our actual patterns
Included the right imports and configuration
That's the difference between "AI code editor" and "AI that knows your codebase."
Cursor vs Copilot: The Real Comparison
With proper context, both tools are effective. Here's where they actually differ:
Cursor wins for:
Deep refactoring across multiple files
Codebase-wide search and analysis
Chat-driven development (when you need to iterate)
Copilot wins for:
Pure autocomplete speed (it's noticeably faster)
Working in any editor (VS Code, JetBrains, Neovim)
Simple completion tasks
I use both. Cursor for feature work and refactoring. Copilot for quick edits and completion.
The Workflows That Actually Work
For New Features
Check Glue to understand the relevant services and their health
Open related files in Cursor for context
Write a detailed prompt referencing specific patterns
Let Cursor generate the skeleton
Use Copilot for filling in the details
For Debugging
Use Glue to find similar code patterns
Check complexity and churn metrics (is this code problematic?)
Ask Cursor to explain the code with full context
Make targeted fixes with Copilot's inline suggestions
For Refactoring
Glue shows you all the places a component is used
Cursor helps you modify the component and update call sites
Copilot handles the repetitive parts of each update
What I Learned
The 10x productivity gain everyone talks about? It's real, but not from the AI itself.
It's from:
Reducing context switching (the AI remembers your patterns)
Eliminating boilerplate (let the AI write the boring parts)
Faster iteration (try approaches quickly, keep what works)
But only if you give the AI enough context to be useful.
Without context, these tools are expensive autocomplete. With context, they're genuine productivity multipliers.
My Current Setup
I run Cursor as my primary editor. Copilot installed as a backup for quick completions. Glue integration enabled so Cursor has codebase intelligence.
When I start work on a new area:
Check Glue for context (what exists, who owns it, health metrics)
Open key files in Cursor to load them into context
Write detailed prompts that reference specific components
Let the AI do the heavy lifting
This workflow gets me through tickets 3-4x faster than I was six months ago. Not because the AI writes all my code. Because it writes the parts I don't want to think about, while I focus on the parts that matter.
The secret isn't picking the right AI tool. It's feeding your AI tools the right context.