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Answer: Use a different semantic similarity search algorithm
The issue is that the RAG system retrieves irrelevant context, leading to incorrect product information in responses. Option D (using a different semantic similarity search algorithm) directly addresses the root cause by improving the retrieval step's accuracy in finding relevant documents. This is supported by the community discussion, where the top-voted comment (4 upvotes) explains that D fixes retrieval relevance, while A (assessing quality) is diagnostic but not a direct fix, B (caching) is unrelated to relevance, and C (using a different LLM) targets generation quality, not retrieval. Although one comment suggests A as a first step, the consensus and question focus on improving relevance, making D the optimal choice.
Author: LeetQuiz Editorial Team
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A Generative AI Engineer at an electronics company has deployed a RAG application for customer product inquiries. User feedback indicates the system frequently provides information about the wrong product. What steps can the engineer take to enhance the relevance of the RAG system's responses?
A
Assess the quality of the retrieved context
B
Implement caching for frequently asked questions
C
Use a different LLM to improve the generated response
D
Use a different semantic similarity search algorithm
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