How does LlamaIndex handle RAG hallucinations compared to LangChain?

All answers to this question.

Grounding is essentially what LlamaIndex was designed for. Their "Query Engines" have built-in "Response Synthesis" modes like compact or refine that are specifically tuned to keep the model focused on the retrieved text. When we built a compliance bot, we used the refine mode, which iteratively goes through chunks to ensure the final answer is fully supported by the data. It also provides a source_nodes attribute in the response, making it very easy to display exact citations and page numbers back to the user.

   Answered 2025-02-07 by Kimberly Clark

Those response modes sound helpful, but do they significantly increase the response time for the end user? I'm worried that an "iterative" refinement process might make the assistant feel too slow for a real-time conversation. Have you benchmarked the latency between the compact and refine modes in a production environment?

   Answered 2025-02-10 by Matthew Garcia

• Matthew, there is definitely a tradeoff. Refine is slower because it makes multiple LLM calls. However, for a medical assistant where accuracy is life-or-death, the extra 2-3 seconds are usually acceptable. If speed is the priority, we usually stick to compact, which stuffs the chunks into one call. LlamaIndex also supports asynchronous processing, which helps mask some of that latency for the user.

     Commented 2025-02-12 by George Harris

LlamaIndex gives you the tools to verify sources, but you still need a good prompt template. It won't stop hallucinations entirely if the model isn't told to stay silent when it doesn't know the answer.

   Answered 2025-02-14 by Jason Lee

• Exactly, Jason. I always add "If you don't find the answer in the context, say you don't know" to my LlamaIndex prompt templates. It’s a simple but effective safety net.

     Commented 2025-02-16 by Laura Bennett