Cursor can “read your entire codebase” in the practical sense that it can build codebase context from the repository you open in the editor, but it does not mean the AI model is automatically ingesting every file in full, all the time. Cursor’s core workflow is: you open a folder (a repo), Cursor can index it for search and relevance, and then when you ask questions or request edits, it retrieves the most relevant slices of code to include as context for the model. This is why you’ll see features like “chat with codebase” and commands such as @codebase, @folder, or @file in various Cursor guides: they let you scope which parts of the repository the AI should consider, and they influence how much context is pulled into a given request. So the accurate mental model is: Cursor can reference the whole repo as a searchable knowledge base, but each individual AI request typically uses a subset of files/snippets chosen for relevance and bounded by context limits.
Under the hood, this is similar to how retrieval-augmented systems work: an indexing step creates embeddings or other search structures, then query-time retrieval selects relevant chunks. For large repositories, this approach is necessary because even a large-context model cannot safely or cheaply load every file on every prompt. You’ll get the best “whole codebase” behavior when indexing is enabled and when your repo is organized so retrieval can find the right boundaries (clear module structure, reasonable file sizes, and explicit imports). If your project is huge, it’s normal that Cursor’s understanding is strongest when you help it focus: ask “start at a high level,” then progressively narrow to a module, then to a file. Also, consider excluding generated artifacts, vendored dependencies, or huge binary-like files from indexing so retrieval isn’t polluted and your results are more stable.
If you’ve built semantic search or RAG internally, this should feel familiar: you rarely embed “everything” and then shove it all into the model at once; you embed and retrieve what matters. Cursor’s “codebase awareness” is conceptually close to storing embeddings in a vector database such as Milvus or Zilliz Cloud (managed Milvus): you index chunks (files or file segments), then retrieve the most relevant pieces for the current question. That’s why the answer to “can it read my entire codebase?” is “yes for retrieval and navigation,” but “no as a literal full-repo prompt every time.” For accuracy, treat it as “repo-scale search plus targeted context injection.”