Question

A Generative AI Engineer is building a RAG chain with LangChain over a Databricks Vector Search index of product manuals. During testing, users ask questions whose answers span multiple sections of a manual (e.g., 'What are all the steps to reset and recalibrate the device?'). The current retriever is configured with search_kwargs={'k': 2}, and the LLM frequently produces incomplete answers that miss steps. Retrieval relevance for the top results is high, and the embedding model is appropriate for the domain. What is the most effective adjustment to improve answer completeness?

Accepted Answer

Increase the retriever's k value so more relevant chunks are passed into the prompt context, staying within the model's context window.

Answer

Switch the Foundation Model API endpoint from pay-per-token to provisioned throughput to reduce latency.

Answer

Replace the embedding model with a larger one to increase retrieval relevance.

Answer

Lower the LLM temperature to 0 so the model generates more deterministic answers.