Raw LLM Responses

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G this had to be the single corniest comment I've ever read in 2025 I genuinely t… ytr_UgzJDWFAw… G We do not know what we do not know. Humans have many facets . Women do not under… ytr_UgyRdMRH0… G While I’ll say using AI to make art isn’t the worst thing, maybe for idk landsca… ytc_UgwLAwbmq… G 0:25 - 0:26: AI version of Taylor Swift: “TayAILor Swift”… I think. And… I thi… ytc_UgwijCUjO… G I get the feeling that once the AI gets to a point of advancement, it will simpl… ytc_Ugwy2vGC9… G ​@mrownagelolremember ai learned to write from popular formats. That style of no… ytr_UgxrhDJ4m… G This is a bad hit piece I find it funny that Waymo has more issues than Tesla an… ytc_UgzIJYHpQ… G Uhhh ✨️quick✨️ question ...what if she decides to turn on us😳.WAIT! actually we… ytc_UgyBxQArn…
Comment
here are the secret words: "Improve up on that" and that is your second prompting. For first you must do these steps: Best Practices for Effective LLM Prompting Successful prompting of large language models requires precision, clarity, and a strategic approach. Every query should be directed toward a clear goal, eliminating ambiguity and unnecessary information. Providing contextual details ensures that the model understands the purpose of the response, while structuring the prompt in a logical format leads to optimal results. Assigning a specific role to the model enables content generation from the desired perspective, whether it involves technical analysis, a summary, or creative interpretation. An iterative approach is essential for refining responses—adjusting the prompt based on previous results leads to more precise and useful information. In environments where the model supports memory, referencing previous responses ensures consistency and continuity in content generation. Experimenting with prompt formulations is not optional but essential—only through adaptation and testing can maximum efficiency in LLM utilization be achieved.
youtube AI Moral Status 2025-03-31T17:0…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-27T06:26:44.938723
Raw LLM Response
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