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Question 2. Which statement best describes the role of Retrieval Augmented Generation (RAG) in enhancing LLM trustworthiness?
  • RAG automatically removes biased content from model outputs.
  • RAG improves factual accuracy by grounding responses in verified external resources.
  • RAG increases computational efficiency to reduce energy consumption.
  • RAG restricts sensitive information in the knowledge base.
Answer: RAG improves factual accuracy by grounding responses in verified external sources. Explanation Retrieval-Augmented Generation (RAG) enhances the trustworthiness of large language models by providing external, contextual evidence that the generator conditions on. Instead of depending solely on the model’s internal weights, RAG retrieves relevant documents or passages from a curated knowledge base and uses those retrieved items to ground responses. This reduces unsupported claims and lowers the risk of hallucination. Key points:
  • The retrieval step supplies current or domain-specific evidence for the generator to reference, increasing factual accuracy and traceability.
  • The effectiveness of RAG hinges on retrieval quality: indexing, search relevance, and the quality of source material directly affect output reliability.
  • RAG is not a silver bullet for bias or data-sensitivity issues — these require explicit curation, filtering, and governance at the source and application layers.
  • For higher trustworthiness, combine RAG with source citations, verification strategies, and downstream moderation/policy controls.
Comparison of options References and further reading
RAG helps make outputs more factual by grounding answers in retrieved evidence, but it is only as reliable as the sources and retrieval strategy. Validate and curate the knowledge base and include citation/verification steps for higher trustworthiness.
The image is a question about the role of retrieval-augmented generation (RAG) in enhancing LLM trustworthiness, with a highlighted answer explaining that RAG improves factual accuracy by grounding responses in verified external sources.

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