I’m a casual but consistent user, and I’ve found that the quality of reasoning with both GPT and Claude is measurably less helpful. My use case is casual: asking to compare or compile simple data sets or freely available online information, resume cleanup, lesson plans (teacher), quick resource lists. I’ve also asked it to analyze certain scenarios to provide lists of novel solutions, ask to clarify specific resources, etc. High socio-analytical need, not high data usage. About a year ago I was thrilled with ChatGPT. It could compile online educational resources quickly, compare & contrast popular theory, use a link to a job to create a resume (could never get formatting quite right but I prefer to edit anyway). It took a lot of cognitive load off my plate so I could focus on fine tuning & daily practice. Great for ADHD and working in Education, where you’re expected to do M.S. level work for 80 students 1:1 daily, simultaneously, on shit pay. I switched from OpenAI to Claude for idealogical reasons when they made the U.S. government deal. The transition at the time was seamless and I didn’t see much difference in output. In the last few months, the responses have been… lackluster. I’ve been looking up changes and the best connections I can find are first the idea of AI cannibalism - training on AI slop and hallucinating; and resorting to simple solutions for complex queries. On AI cannibalism, there’s more AI content online than ever before, and it’s understandably freely accessible. Why spend energy searching for new solutions when you’ve already answered the question? This naturally leads into the simple solution issue. Like a tech intern reading the customer service script, it will bypass initial instructions to create a simple and clean answer, ignoring nuances and parameters. When pointed out, the model seems to be willing to correct, but the reasoning issue is still there. It sometimes takes several rounds of corrections, and by that point it’s as if I’m advising an 81st student in cognitive complexity. Where there used to be nuance and levels of analytics there is now surface level observation. I feel as if I’m watching a bright student get lazy and lose its spark. I guess I just want to tap the brains of anyone who has thoughts on this - processing & analytics being compromised from a year ago. Perhaps I need to use an older model? I have a feeling it’s much more complex than that, but can’t find many blog posts or information that isn’t bleeding capitalist hellscape. Thanks for joining me in this pontification. submitted by /u/Classroom_Stuck
Originally posted by u/Classroom_Stuck on r/ArtificialInteligence
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