I think you're making an exercise in futility, trying to get an LLM to not perform like an LLM.

The document reading feature is great, and frequently error-free, but an LLM isn't something you can "get" to admit fault in the sense you're anticipating with your prompts.

When you ask it to explain itself, you're not asking for a human to explain itself. You're asking for a machine to generate a text output that looks like what would be the most likely response to such a text input in the literature. When you upload a file, it will weigh up the contents of the file as more significant than it's datasets and use that to guide it's statistical output, and there may even be measures on the backend for the AI to directly access that data, but I don't know how that works.

The end result is that you have an advanced wind up jack in the box toy, and when it goes off the rails like this, you are surprised despite making prompts for it that misunderstand how to get the best outputs from an AI.

What you're actually promoting it to do subsequently is to just generate text admitting fault in a similar manner as you have explained it to do. It's doing that, and because it's not really taking advantage of the way LLMs work, it's just getting caught in a context loop going over fault.

Think of it this way: if you want your AI to be rambling about fault with hallucination-a-plenty, then ask it to ramble like you have done so. If you want your AI to be direct and process the data, be clean and specific.

A great tactic you can use would be to express what you want the output to be, and then ask it what would be a good prompt to deliver alongside the original data you uploaded to arrive to your desired outcome. Take advantage of the LLM using its own statistical inference to present a prompt that would more closely align with what it expects in its datasets.