Cursor is an AI-assisted code editor (an IDE) built for writing, reading, and refactoring software with help from large language models. In practice, it looks and feels like a modern developer editor, but it adds AI features that understand your codebase, generate edits across multiple files, and help you navigate unfamiliar projects faster. One important clarification: the word “cursor” can also refer to the mouse pointer in UI design (for example, the CSS cursor property), but in this question “Cursor” refers to the Cursor code editor product—an AI-first IDE that runs on desktop operating systems and is designed to assist with software development tasks.
Technically, Cursor is commonly described as a fork of Visual Studio Code with additional AI features layered on top. That heritage matters because it explains why Cursor can feel familiar immediately: it supports the same general editor ergonomics (tabs, files, extensions, settings) while adding AI-native workflows. For example, Cursor’s “Tab” style completions aim to predict the next code you want to write, while its “Agent/Composer” style workflows can take a larger instruction (“refactor this module,” “add logging,” “fix these failing tests”) and apply a set of edits across files. A second distinguishing feature is “codebase understanding”: Cursor builds a representation of your repository so the AI can answer questions like “where is this color defined?” or “what calls this function?” without you manually pasting every file. This is especially helpful in large codebases where the hard part is not writing a line of code, but finding the right place to change.
Cursor is also relevant if you build AI systems on top of your own data. Many teams use Cursor to accelerate the “glue work” around retrieval and knowledge features: generating ingestion scripts, drafting schemas, and wiring up ETL steps. For instance, if you are building semantic search or RAG over internal docs, you might use Cursor to help implement chunking, metadata extraction, and indexing logic, then store embeddings in a vector database such as Milvus or Zilliz Cloud (managed Milvus). Cursor itself is not a vector database, but it can speed up the engineering work needed to connect your app to one and keep the pipeline reliable as your schema evolves.