AI-201 · Question #204
Universal Containers wants to keep retrieval accurate as product documentation changes frequently. Which approach should the company implement?
The correct answer is B. Rebuild the search index. In retrieval-augmented generation (RAG) systems, documents are chunked and converted to vector embeddings stored in a search index. When source documents change, the old embeddings become stale and will return outdated or incorrect results. Rebuilding the search index…
Question
Universal Containers wants to keep retrieval accurate as product documentation changes frequently. Which approach should the company implement?
Options
- ALeave embedding unchanged even if content is updated.
- BRebuild the search index.
- CManually delete the stale data chunks.
How the community answered
(47 responses)- A4% (2)
- B94% (44)
- C2% (1)
Explanation
In retrieval-augmented generation (RAG) systems, documents are chunked and converted to vector embeddings stored in a search index. When source documents change, the old embeddings become stale and will return outdated or incorrect results. Rebuilding the search index re-processes updated content into fresh embeddings, keeping retrieval accurate. Leaving embeddings unchanged (A) guarantees stale results. Manually deleting stale chunks (C) is error-prone, incomplete, and not scalable for frequently changing documentation-rebuilding the index is the correct, systematic solution.
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