How Does Cursor Work? Inside the Architecture of an AI Coding Assistant

One‑sentence definition

Cursor is an engineering system that turns IDE actions into structured context and feeds it to an LLM.

Core architecture: four layers

The system consists of:

┌─────────────────────────────────┐
│   IDE Interaction Layer (client)│ ← selection, input, shortcuts
├─────────────────────────────────┤
│   Context Construction Layer    │ ← most critical, decides what LLM sees
├─────────────────────────────────┤
│   Model Routing Layer           │ ← selects model, handles streaming
├─────────────────────────────────┤
│   Code‑write Layer             │ ← diff application, conflict handling
└─────────────────────────────────┘

Most users only notice the first and fourth layers; the middle two constitute the technical core.

Context Construction Layer: the core engine

1. Current file + cursor position (mandatory)

Only a window around the cursor is taken (e.g., 100 lines before, 50 lines after) and given the highest priority.

// Cursor builds context – pseudo code
function buildContext(editor: Editor): ContextChunk[] {
  const chunks: ContextChunk[] = [];
  // 1. current file, limited to cursor vicinity
  const cursorLine = editor.getCursorLine();
  chunks.push({
    type: 'current_file',
    content: editor.getLines(cursorLine - 100, cursorLine + 50),
    priority: 10 // highest priority
  });
  return chunks;
}

2. Semantic retrieval (codebase indexing)

Cursor maintains a local vector index of the whole project and performs a RAG‑style search using the user's natural‑language query.

async function retrieveRelatedChunks(query: string, index: VectorIndex, budget: number): Promise<ContextChunk[]> {
  const results = await index.search(query, { topK: 20 });
  const selected: ContextChunk[] = [];
  let usedTokens = 0;
  for (const result of results) {
    const tokenCount = estimate(result.content);
    if (usedTokens + tokenCount > budget) break; // stop when budget exceeded
    selected.push(result);
    usedTokens += tokenCount;
  }
  return selected;
}

3. @‑symbol explicit references

Using @file, @folder or @docs injects specific files or documentation with higher priority than automatic retrieval.

// Example of manual context injection
@src/utils/auth.ts  Help me rewrite this function to support OAuth2

The quality of your @‑references directly determines output quality.

Cmd+K vs Chat: two different context strategies

Cmd+K (inline edit)

Context = selected code + a few surrounding lines + short instruction
Goal = precise local modification, minimal token usage

const inlineContext = {
  selected: selectedCode,
  before: codeBeforeCursor(30), // 30 lines before
  after: codeAfterCursor(15),   // 15 lines after
  instruction: userInstruction
  // No project‑wide information
};

Chat (conversation mode)

Context = conversation history + retrieved chunks + explicit @‑references + current file
Goal = complex reasoning, cross‑file understanding, higher token usage

const chatContext = {
  history: conversationHistory,
  retrieved: await retrieveRelated(),
  explicit: parseAtReferences(),
  currentFile: getCurrentFile()
};

Conclusion: Use Cmd+K for small, precise edits; use Chat together with @‑references for cross‑file tasks.

Token consumption comparison

Cmd+K – low (~2K tokens) – suitable for local rewrite, formatting, renaming.

Chat – high (~20K+ tokens) – suitable for architecture discussion, multi‑file refactoring, debugging.

Composer – highest (~50K+ tokens) – suitable for full‑stack modifications and multi‑file coordination.

Codebase Indexing: how it "reads" your project

Cursor splits the codebase by function/class/file, embeds each chunk into vectors, stores them in a local vector DB (similar to FAISS), and at query time converts the natural‑language request into a vector to find nearest neighbours.

Project code
  ↓ chunk by function/class/file
  ↓ embedding (to vector)
  ↓ store in local vector DB
  ↓ query: natural language → vector → nearest‑neighbor → return snippets

# Simplified implementation of Cursor's index
class CodebaseIndex:
    def __init__(self, project_root: str):
        self.chunks = []
        self.embeddings = []

    def index_file(self, file_path: str):
        code = read_file(file_path)
        chunks = split_by_ast(code)  # split by AST, not line count
        for chunk in chunks:
            embedding = embed(chunk.content)  # call embedding API
            self.chunks.append(chunk)
            self.embeddings.append(embedding)

    def search(self, query: str, top_k=10):
        query_embedding = embed(query)
        scores = cosine_similarity(query_embedding, self.embeddings)
        top_indices = scores.argsort()[-top_k:][::-1]
        return [self.chunks[i] for i in top_indices]

Important detail: The index is stored locally; Cursor does not upload your code to the cloud for indexing.

Diff‑apply layer: how it "writes" your code

The LLM returns a structured diff rather than raw code. Cursor applies edits from the end of the file backwards to avoid line‑number drift.

interface DiffEdit {
  type: 'insert' | 'delete' | 'replace';
  startLine: number;
  endLine: number;
  newContent: string;
}

async function applyDiff(editor: Editor, edits: DiffEdit[]) {
  // Apply from later lines to earlier lines
  const sorted = edits.sort((a, b) => b.startLine - a.startLine);
  for (const edit of sorted) {
    if (edit.type === 'replace') {
      editor.delete(edit.startLine, edit.endLine);
      editor.insert(edit.startLine, edit.newContent);
    }
  }
  await editor.format(affectedRange(edits));
}

Applying diffs in reverse order is crucial; otherwise earlier inserts shift line numbers for later edits.

Common pitfalls (learned the hard way)

Pitfall 1: In large projects the index may be incomplete, causing irrelevant files to be retrieved.

// 500 files in project, only 10 are relevant
// Retrieval may return top‑ranked but unrelated files

Fix: Manually specify relevant files with @file.

@src/services/payment.ts @src/types/order.ts
Help me add a timeout‑retry logic in processOrder

Pitfall 2: Inline edit loses context when the selected function depends on types defined elsewhere.

// Only the function is selected
function calculateTax(order: Order): number { ... }
// LLM doesn't know where Order is defined

Fix: Expand selection to include dependent definitions or switch to Chat + @file.

Pitfall 3: Long chat histories cause the model to forget earlier decisions. Fix: Start a new Chat for each major feature and inject relevant files with @file at the beginning.

@CLAUDE.md @src/architecture.md
I want to start the user‑auth module, read the project conventions first

Pitfall 4: Overly long .cursorrules files get ignored. Fix: Keep rules under 30 lines and include only project‑specific conventions.

- API routes live in src/api/, never fetch directly in components
- Error handling uses Result<T, E> instead of throw
- DB access must be in src/services/, not in controllers

Pitfall 5: Composer mode may introduce regressions across files. Fix: Run a type‑check after Composer finishes.

tsc --noEmit && echo "✅ Type check passed" || echo "❌ Type errors found"

Takeaway checklist

Use Cmd+K for small, precise edits; use Chat for complex, cross‑file tasks.

In large codebases, manually add @file references to keep context relevant.

Open a new Chat for each feature; avoid a single window with dozens of turns.

Keep .cursorrules under 30 lines and focus on project‑specific policies.

After Composer changes, run a type‑check; never trust the output blindly.

Prefer @docs to pull library documentation instead of relying on the model's memory.

Wait for the vector index to finish building before starting Chat interactions.

Conclusion

We broke down Cursor into its four layers, highlighted the three‑pronged context construction (current file, semantic retrieval, @‑references), compared Cmd+K and Chat token usage, explained the local vector indexing mechanism, and detailed the reverse‑order diff application. The key insight is that the AI receives a concise "brief"; the quality of your @‑references determines the quality of the output.