How can an LLM (Large Language Model) be trained on live data to generate real-time answers?

Beginners
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I’m looking for guidance on setting up a pipeline or framework to train an LLM using live data streams, such as data coming from IoT devices, social media, or API endpoints. The goal is to have the model continuously generate relevant and accurate answers in real-time.

Additionally, I’m curious about the challenges involved in handling live data for LLM training, such as latency, ensuring data consistency, and avoiding overfitting. Are there specific techniques, tools, or platforms that work best for this use case? Any insights or recommendations would be greatly appreciated!

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If you need it for a specific domain, such as generating real-time answers about Bitcoin, Agents and/or RAG might be helpful. For general real-time LLM capabilities, however, I don’t believe it’s feasible with the current architecture and technology.