Modern development teams often work with massive repositories—monorepos, microservice clusters, deeply nested folder structures, and legacy components. For AI-powered coding assistants to be genuinely useful at this scale, they must process large codebases intelligently without overwhelming the underlying language model. Both GitHub Copilot and Cursor have evolved advanced techniques to understand context, index files, and deliver accurate suggestions quickly.

1. Contextual Code Understanding Instead of Full Repository Loading

LLMs cannot take an entire repository as input. Instead, both Copilot and Cursor perform smart context extraction:

• They detect which files are currently open, edited, or referenced.

• They prioritize symbols, functions, classes, and modules relevant to the file in focus.

• Instead of reading thousands of files, they dynamically build a compact “context window” that fits within the model’s token limits.

This ensures the model only sees what actually matters for the task at hand.

2. Background Indexing for Fast Symbol and Reference Lookups

GitHub Copilot

GitHub has integrated advanced indexing via the GitHub Code Graph.
It creates a semantic map of:

• API calls

• Type definitions

• Imports and exports

• Cross-file references

This allows Copilot to quickly understand relationships between files without needing to re-process the repository repeatedly.

Cursor

Cursor uses a local background indexer that scans the project structure and builds embeddings (vector representations) for files.
This enables the tool to:

• Retrieve relevant code instantly

• Provide file-aware refactoring

• Answer repository-level questions

Cursor’s indexer is extremely fast and works offline, making it ideal for huge codebases.

3. Embedding-Based Retrieval for Relevant Context

Both platforms use Retrieval-Augmented Generation (RAG).
This means:

1. Your query (like “fix this bug” or “add logging”) is converted into an embedding.

2. The system compares it to embeddings of repository files.

3. Only the most relevant files are sent to the LLM.

This dramatically improves accuracy, especially in large monorepos where multiple files share similar names or patterns.

4. Understanding Project Structure and Dependencies

Copilot and Cursor intelligently map:

• Folder hierarchies

• Build systems

• Package dependencies

• Framework structures (React, Django, Spring Boot, etc.)

This allows them to deliver framework-aware suggestions instead of generic code completions.

For example:

• Suggesting the correct Redux action import

• Identifying the right Django model file

• Locating configuration files that influence runtime behavior

5. Incremental Learning from Developer Behavior

Both tools learn from:

• Which suggestions developers accept

• Which files developers frequently open together

• How developers structure code

This builds a personalized understanding of the codebase.

Cursor goes further by allowing customizable project rules, enabling the LLM to respect naming conventions, architectural patterns, or coding standards unique to the team.

6. Efficient Multi-File Editing and Refactoring

Cursor’s standout feature is multi-file editing:

• You can ask it to refactor an entire subsystem.

• It identifies the impacted files.

• It applies changes across the repository in a controlled, reviewable way.

Copilot is adding similar capabilities but currently focuses more on inline suggestions.

Final Thoughts

GitHub Copilot and Cursor handle large codebases efficiently by combining:

✔ Smart contextual filtering
✔ High-performance code indexing
✔ Embedding-based retrieval
✔ Structural project awareness
✔ Developer behavior learning
✔ Multi-file reasoning and refactoring