RAG (Retrieval Augmented Generation)
Retrieval Augmented Generation is a technique where AI retrieves relevant information from external sources before generating a response. Instead of relying only on training data, RAG systems search documentation, codebases, or databases to ground their answers in current, specific information.
Example
When Cursor answers a question about your code, it first retrieves relevant files from your project (retrieval), then uses that context to generate an informed answer (generation) — that's RAG in action.
RAG addresses a fundamental AI limitation: models only know what was in their training data. RAG systems can access current, specific information — making AI actually useful for real codebases.
How RAG Works
User Question
↓
[Retrieval] → Search relevant documents/code
↓
[Augmentation] → Add retrieved content to prompt
↓
[Generation] → AI generates response with context
Why RAG Matters for Coding
Without RAG:
- AI doesn't know your codebase
- Suggestions based only on general knowledge
- Can't reference your specific APIs or patterns
With RAG:
- AI searches your actual code
- Answers grounded in your project
- Suggestions match your architecture
RAG in Vibe Coding Tools
Cursor uses RAG to:
- Find relevant code files for context
- Search documentation
- Index your codebase for semantic search
You benefit by:
- Not manually pasting code into prompts
- Getting project-aware suggestions
- AI that "knows" your codebase
RAG vs Larger Context Windows
Both help AI access more information:
| RAG | Large Context |
|---|---|
| Retrieves relevant info | Fits more in one prompt |
| Selective and focused | Everything available |
| More scalable | Bounded by window size |
| Requires good retrieval | Simpler to use |
Best tools combine both — large context windows with intelligent RAG retrieval.
Cursor
Emergent — An agentic vibe-coding platform that builds production-ready apps