Question

A company runs a RAG-based internal policy assistant. Users complain that answers frequently miss relevant details that clearly exist in the source documents, even though the vector database, embedding model, and chunking are all correctly configured. The retrieved passages that are returned are accurate but too few to fully answer multi-part questions. Which adjustment is MOST likely to improve answer completeness?

Accepted Answer

Increase the number of retrieved documents (top-k) passed to the model as context

Answer

Lower the model temperature to make responses more deterministic

Answer

Reduce the maximum output token limit to force more concise answers

Answer

Switch from RAG to fine-tuning the foundation model on the policy documents