How Codebase Indexing Works
When you open a project in an AI coding tool, the indexer scans the repository. It reads files, parses functions, classes, imports, and types, then creates searchable data that the AI can query when generating suggestions.
Most indexers convert code into vector embeddings. Each file or code chunk gets a numerical fingerprint that captures what it means. When the AI needs context for a suggestion, it searches the embedding space for code chunks related to the current task and loads them into the context window.
Some tools index locally on your machine. Others send code to cloud servers for processing. A few use a hybrid approach where initial parsing happens locally but embedding generation runs in the cloud. Each approach has different tradeoffs around privacy, speed, and quality.
What Makes Good Indexing
Speed matters for the first index. Opening a large project should not mean waiting 10 minutes before the tool becomes useful. Cursor indexes most repositories in under a minute. Some tools take longer for monorepos with hundreds of thousands of files.
Incremental updates matter more than initial speed. After the first index, the tool must track file changes and update its index as you edit, create, and delete files. If the index becomes stale, the AI suggests code based on an outdated view of the project.
Retrieval accuracy determines whether the right files end up in the context window. A large index is useless if the retrieval algorithm selects irrelevant files. The best indexers understand dependency chains, so when you edit a function, they pull in the functions that call it, the types it uses, and the tests that cover it.
Indexing Approaches Across Tools
Cursor uses local embedding-based indexing. It creates a vector index of your project on your machine and searches it to find relevant context for each suggestion. Files stay on your device during indexing.
Claude Code takes a different approach. Rather than pre-building a vector index, it reads relevant files into the context window on demand using file search and grep-like retrieval. The model itself decides which files to examine based on the task.
GitHub Copilot uses a combination of the current file, open tabs, and neighboring files. Its indexing is lighter than Cursor’s but relies more on the developer having relevant files already open.
The tradeoffs are real. Local indexing keeps code private but is limited by your machine’s compute. Cloud indexing handles larger repositories but requires sending code to external servers. On-demand retrieval avoids index staleness but consumes more tokens per request.
When Indexing Fails
Indexing is not perfect. Large monorepos with millions of lines of code can overwhelm indexers that were designed for single-package repositories. Files in unusual formats, generated code, and binary assets can pollute the index with irrelevant content.
Most tools let you configure what gets indexed through ignore files (similar to .gitignore). Excluding node_modules, build directories, vendor bundles, and generated code from the index improves both indexing speed and retrieval quality. The smaller the index, the more likely the retrieval algorithm selects relevant results.
Stale indexes are a subtler problem. If you switch branches, pull changes from remote, or generate new files outside the editor, the index may not reflect the current state of the project until it rebuilds. Some tools handle this automatically. Others require a manual re-index. Knowing how your tool handles this prevents confusion when the AI suggests patterns from code that no longer exists.