What does the RAG Sequence model do in the context of generating a response?
A.
It retrieves a single relevant document for the entire input query and generates a response based on that alone.
B.
For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
C.
It retrieves relevant documents only for the initial part of the query and ignores the rest.
D.
It modifies the input query before retrieving relevant documents to ensure a diverse response.
The Answer Is:
B
This question includes an explanation.
Explanation:
Comprehensive and Detailed In-Depth Explanation:
The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect—RAG considers the full query. Option D is false—query modification isn’t standard in RAG Sequence. This method enhances response quality with diverse inputs.
OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.
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