What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines the power of large language models with external knowledge retrieval. Instead of relying solely on trained knowledge, RAG models fetch relevant information at query time.
How RAG Works
- Query Processing: User question is analyzed
- Retrieval: Relevant documents are fetched from knowledge base
- Augmentation: Retrieved context is added to the prompt
- Generation: LLM generates response using context
RAG vs Fine-Tuning
| Aspect | RAG | Fine-Tuning |
|---|---|---|
| Knowledge Updates | Instant | Requires retraining |
| Cost | Lower | Higher |
| Transparency | Can cite sources | Black box |
Enterprise Applications
- Internal knowledge search
- Customer support with documentation
- Policy compliance checking
- Research assistance
Implementation Guide
Start with high-quality document indexing. RAG is only as good as its retrieval system.