What are the best practices to minimize hallucinations in LLM inference pipelines when using HF models?

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When using large language models (LLMs) from Hugging Face, how can we reduce the chance of the model making things up (hallucinating) when giving answers?

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It’s nearly impossible or inefficient to reliably prevent hallucinations using current LLMs alone, so for that purpose, I personally recommend exploring RAG or RAG-like approaches.
Using existing frameworks like LangChain with Hugging Face models would likely require the least effort.