Universal Containers wants an AI agent to answer questions about warranties using unstructured data stored in Data Cloud. Results must be filterable by product line and ranked by recent updates.
A.
Use the default retriever which automatically accounts for recency ranking.
B.
Build a custom retriever in Einstein Studio with product line filters and recency ranking.
C.
Apply semantic embeddings with default metadata filters to achieve the desired result.
Comprehensive and Detailed Explanation From Exact Extract:
The guide on RAG and search indexes indicates that if you need finetuned retrieval behaviour (such as filtering by product line and ranking by recency), you should build a custom retriever. The documentation states: “You can add ranking factors such as recency and popularity at the time of index creation … Use prefilter fields and ranking factors.” Also: “When you create a search index, Data 360 automatically creates a default retriever … you can create custom retrievers in Einstein Studio to refine search criteria.” Hence to satisfy filtering by product line and recency ranking, the correct answer is B. Option A (default retriever) does not guarantee the filter/ranking customization; Option C (semantic embeddings with default metadata filters) may offer some filter capability but doesn’t explicitly provide ranking by recency and fine filter by product line. Thus B is correct.
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