What are the primary challenges when deploying Large Language Models for enterprise-scale RAG?

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Hallucinations in RAG usually stem from poor retrieval quality or the model's "temperature" being set too high. First, look at your chunking strategy; using a "Recursive Character Text Splitter" with an overlap of about 10-15% helps maintain context between chunks. Second, consider a "Re-ranking" step. After your vector database (like Pinecone or Milvus) returns the top results, use a smaller, faster model to re-score those documents for relevance before passing them to the LLM. This significantly reduces the noise and improves the overall accuracy of the final generated answer.

   Answered 2025-06-12 by Pamela Simmons

Are you currently using a semantic cache to reduce latency for common queries, and what is your average retrieval time from the vector store?

   Answered 2025-06-15 by Daniel Ortiz

• Daniel, we just implemented Redis for caching, and it cut our response time in half for frequent HR policy questions. However, the retrieval time from the vector store is still around 200ms, which feels a bit sluggish. We are experimenting with different embedding models to see if a smaller vector dimension can speed things up without sacrificing too much semantic meaning.

     Commented 2025-06-18 by Kevin Murphy

Evaluation is the hardest part. I recommend using a framework like RAGAS to quantify your system's faithfulness and relevance. You can't improve what you don't measure.

   Answered 2025-06-20 by Susan Gray

• Totally agree, Susan. Using RAGAS helped us identify that our "context precision" was low, leading us to fix our metadata tagging, which made a world of difference.

     Commented 2025-06-22 by Alice Henderson