Question 1
[LLM Integration and Deployment]
What is Retrieval Augmented Generation (RAG)?
- AOption A:
RAG is an architecture used to optimize the output of an LLM by retraining the model with domain-specific data.
- BOption B:
RAG is a methodology that combines an information retrieval component with a response generator.
- COption C:
RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
- DOption D:
RAG is a technique used to fine-tune pre-trained LLMsfor improved performance.
Show answer and explanation
Correct answer: B
- Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large
- language models (LLMs) by integrating an information retrieval component with a generative model.
- As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an
- external knowledge base (e.g., using dense vector representations) and uses them to inform the
- generative process, enabling more accurate and contextually relevant responses. NVIDIA’s
- documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference
- Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data,
- especially for tasks requiring factual accuracy or domain-specific knowledge. Option A is incorrect
- because RAG does not involve retraining the model but rather augments it with retrieved data.
- Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning,
- which is a separate process.