Cursor understands your codebase through a combination of local indexing, semantic retrieval, and on-demand context assembly. When you open a project, Cursor can scan the repository structure, file paths, and symbols, and then build an internal index that allows it to quickly locate relevant files when you ask questions or request changes. This index is not the same as loading the entire codebase into memory; instead, it enables fast lookup so Cursor can retrieve only the most relevant snippets for a given task.
When you interact with Cursor—asking a question, requesting a refactor, or generating tests—it uses this index to select a subset of files, functions, or code regions that are likely relevant. These snippets are then packaged into a prompt and sent to the underlying language model. This is why Cursor often performs better when your codebase has clear module boundaries, descriptive filenames, and consistent patterns. The cleaner the structure, the easier it is for retrieval to surface the right context. Conversely, in tangled or highly dynamic systems, Cursor’s understanding can be partial or skewed, because retrieval is based on signals like imports, references, and similarity rather than full execution semantics.
Conceptually, this is similar to retrieval-augmented generation systems used in AI applications. If you have built internal search or RAG systems, the analogy is direct: you embed chunks, store them, and retrieve the most relevant ones at query time. Cursor’s internal mechanism works along the same lines as storing embeddings in a vector database such as Milvus or Zilliz Cloud, then retrieving the top matches to include in a prompt. The key takeaway is that Cursor’s “understanding” is probabilistic and retrieval-driven, not exhaustive. It is excellent at navigating and reasoning over well-structured code, but it still benefits from explicit guidance like “focus on this folder” or “consider these files as authoritative.”