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RAG Architecture
Retrieve then generate — when and how it beats fine-tuning.
RAG Architecture
RAG couples a retriever with a generator so answers can cite fresh or private knowledge.
Pipeline
- Ingest & chunk documents
- Embed chunks into a vector index
- Retrieve top-k for a query
- Build a grounded prompt
- Generate + cite
When RAG wins
- Knowledge changes often
- You need attributions
- Fine-tuning is too heavy or risky for private data