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Message: <blockquote data-quote="Jacob Lewis" data-source="post: 9699365" data-attributes="member: 6667921">Happy to help, if I can.Going back to your original post, I noticed you mentioned using Gemini with a document on Google Drive as part of your setup. That’s helpful context—and it brings up a broader point I think is worth flagging for anyone following the discussion:Not all LLMs operate the same way.Most of what I’ve shared is based on my experience with ChatGPT, which has its own quirks around memory, document handling, and response behavior. I’m also using a Plus account, which gives access to better tools and options than the free tier. Gemini, from what I understand, integrates more directly with tools like Docs and Drive, and may handle file access a bit differently—possibly reading and applying document context more seamlessly than a typical external upload or injected prompt.That said, the challenges we’re talking about—misaligned expectations, reliance on implicit recall, blurred boundaries between roles—aren’t model-specific. They show up across systems when we assume the tool “knows” more than it actually does, or when we forget that inference is not the same as fact recall.If anything, the differences between models just highlight how important it is to understand the principles behind how these systems operate—not just the surface behavior. Because whether it’s ChatGPT, Gemini, Claude, or anything else, the user still plays a huge role in shaping the outcome.So without knowing first-hand how Gemini handles context or instruction, I can’t say with confidence that my experience with ChatGPT will fully translate—but I hope some of the broader insights are still helpful.</blockquote>

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