GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #15
GENERATIVE-AI-ENGINEER-ASSOCIATE Question #15: Real Exam Question with Answer & Explanation
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Question
A Generative AI Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user's query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values. Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)
Options
- AChange embedding models and compare performance.
- BAdd a classifier for user queries that predicts which book will best contain the answer. Use this to
- CChoose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes
- DPass known questions and best answers to an LLM and instruct the LLM to provide the best
- ECreate an LLM-as-a-judge metric to evaluate how well previous questions are answered by the
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