💻 Why does Cursor understand your code so well? The answer is RAG with semantic search.
Cursor doesn’t “read” your entire code on every query. It indexes it intelligently with a 3-step RAG pipeline:
1. 🔪 Semantic chunking Splits code into coherent units: functions, classes, logical blocks. It doesn’t cut arbitrarily by size — it understands code structure.
2. 🧮 Vector embeddings Each chunk is converted into a numerical vector capturing its meaning. This enables search by semantics, not just literal text.
3. 🔍 Contextual retrieval When you write a natural language query, Cursor searches for the most relevant chunks and includes them as context for the LLM.
The result: precise suggestions that “understand” your architecture, global variables, code patterns, and inter-file dependencies.
The index updates automatically when you modify files, keeping context always fresh.
💡 Explanation in a nutshell#
RAG is like having an assistant who reads all your notes before answering a question. Cursor does the same with your code: it divides it into pieces, converts them into mathematical vectors, and when you ask something, it finds the most relevant pieces to give you an intelligent response.
More information at the link 👇
