Raw LLM Responses

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G For those who dont understand the hate behind ai art im gonna try to sum it up a… ytc_UgwM0ziR8… G It can't get dumber, it can only get better. But if the answers are different, i… rdc_jskzc2t G I've seen this all over my code news. And the joke that AI just means actually I… ytc_UgznyZYrm… G I think you’re talking about the future being entrepreneurship which the average… ytc_Ugx093nF-… G As long AI is use for the benefit of humanity that will create merits of it. If… ytc_Ugz0FDksu… G It's not that immigrants steal jobs. It's just the iron law of supply and deman… rdc_d7kvmgt G I didn’t think the movie I-Robot would be real in my lifetime! But I’m slowly ge… ytc_Ugx6Hxr24… G These arguments always assume that the unemployed masses living on the breadline… ytc_Ugxt740aC…
Comment
On hallucination, retrieval-augmented generation (RAG) pipelines are used which add processing steps before generation to incorporate relevant context/information into the LLM's response. This is most effective in narrow, domain-specific chatbots; its pretty easy to set up a RAG pipeline to search through a given companies' terms of use and similar documents (if pre-processed, much harder if not) to create a customer service chatbot. It's more challenging to use this in generalized models, as the requisite knowledge base is far larger, but RAG is why LLMs frequently attach references to their claims now. There are risks of incorporating erroneous information into the underlying knowledge base and retrieving irrelevant information, but RAG remains an effective method of managing hallucination.
youtube AI Moral Status 2025-11-16T02:0…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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