Preventing Hallucinations in Private LLMs
We’ve experimented with wrappers around standard LLMs, but we can't risk the data 'hallucinating' or leaking patient info. I see Artjoker focuses on Private LLMs and MLOps. How do you ensure the model doesn't just make things up?
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When you’re dealing with sensitive data like patient information, the last thing you want is a model “hallucinating” facts or exposing anything it shouldn’t. That’s exactly why working with a gen ai development company https://artjoker.net can make a big difference. They emphasize private LLMs hosted in secure environments and MLOps practices that include data governance, version control, and monitoring. Instead of a generic public model, they fine-tune on your vetted dataset and implement guardrails so outputs stay factual and compliant. Ongoing evaluation and feedback loops are key to reducing hallucinations and protecting confidential data.