Which of the following is a benefit of using Retrieval Augmented Generation?

Question

Options

• AIt allows LLMs to access and utilize information beyond their initial training data.
• BIt enables LLMs to learn new languages without additional training.
• CIt eliminates the need for fine-tuning LLMs for specific tasks.
• DIt reduces the computational resources required for language modeling.

• A
  85% (35)
• B
  2% (1)
• C
  7% (3)
• D
  5% (2)

Explanation

RAG augments an LLM's knowledge at inference time by retrieving relevant documents from an external knowledge base and injecting them into the prompt, allowing the model to answer questions using information that didn't exist (or wasn't included) when it was trained - making A correct.

B is wrong because RAG doesn't teach the model new languages; it only provides context documents, and the model must already understand those languages to use them. C is wrong because RAG and fine-tuning are complementary techniques - RAG adds dynamic context, while fine-tuning changes model behavior/style; neither eliminates the need for the other. D is wrong because RAG actually adds computational overhead (retrieval, embedding, longer prompts), rather than reducing it.

Memory tip: Think of RAG as giving the LLM an open-book exam - it can look things up in real time, but that doesn't change how smart it is or how cheap it is to run.

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