Retrieval-Augmented Generation (RAG)

Q1: How does RAG improve over traditional language models?

RAG combines external retrieval with generation, allowing access to up-to-date and specialized information, reducing hallucinations and increasing factuality.

Q2: Can RAG handle very large document corpora?

Yes, especially when paired with optimized vector search tools like FAISS or Annoy, designed for scalable retrieval.

Q3: Is RAG suitable for real-time applications?

With efficient indexing and hardware acceleration, RAG can serve real-time needs in chatbots, customer support, and interactive systems.

Q4: How do I ensure the retrieved documents are relevant?

Choose high-quality embeddings and retrieval algorithms, and consider fine-tuning retrieval models on domain-specific data.

Q5: Does RAG require extensive computational resources?

Training the components may be resource-intensive, but inference can be optimized for deployment; precomputing embeddings also helps.

Q6: Can RAG be fine-tuned end-to-end?

Emerging research aims at joint training, but currently, most systems train retrieval and generation modules separately.

This FAQ provides a foundational understanding, addressing common concerns for practitioners implementing RAG.