What is Retrieval-Augmented Generation?
RAG combines search with generation for factual responses.
How It Works:
RAG pipelines retrieve relevant documents from a knowledge base, then feed them as context into a generative model to produce grounded answers.
Key Benefits:
- Reduced hallucinations: Answers reference real data.
- Dynamic knowledge: Supports up-to-date content without retraining.
- Scalability: Add new sources easily.
Real-World Use Cases:
- Enterprise Q&A: Answer company policy questions.
- Legal research: Cite statutes in responses.
FAQs
Which search engines work?
Is latency high?
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