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GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #65

A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to th

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Building and Optimizing RAG Pipelines

Question

A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher- ranked documents. Which option should the team consider to optimize this workflow?

Options

  • AUse a reranker to order the documents based on the relevance scores.
  • BModify the prompt to instruct the LLM to order the documents based on the relevance scores.
  • CUse a different embedding model for computing document embeddings.
  • DIncrease the number of documents added to the prompt to improve context relevance.

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Topics

#RAG#Vector Search#Document Reranking#Context Optimization
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