Embeddings
Embeddings are numerical representations of text or code that capture semantic meaning in a format AI can process. They convert human language into vectors (lists of numbers) where similar concepts are positioned close together, enabling AI to understand relationships between code, documentation, and natural language.
Example
When Cursor searches your codebase for relevant context, it converts your question into an embedding and finds code files with similar embeddings — matching by meaning, not just keywords.
Embeddings are the hidden technology that makes intelligent code search and context selection possible. They're how AI tools understand that "authentication" and "login" are related, even though they share no letters.
How Embeddings Work
- Text goes in — A sentence, function, or code block
- Model processes — Neural network analyzes the content
- Vector comes out — Hundreds or thousands of numbers representing meaning
Why Embeddings Matter for Vibe Coding
Intelligent Search:
- Find code by meaning, not just text matching
- "Find where we handle payments" works even if the code says "processTransaction"
Context Selection:
- AI tools select relevant files to include in prompts
- Limited context windows get filled with the most useful information
Code Understanding:
- Similar code patterns have similar embeddings
- AI recognizes when your new code resembles existing patterns
Practical Impact
Without embeddings, AI tools would:
- Only find exact keyword matches
- Include random files in context
- Miss semantic relationships in code
With embeddings:
- Search understands intent
- Context is intelligently selected
- AI "gets" what you're trying to do
Cursor
Emergent — An agentic vibe-coding platform that builds production-ready apps