Yes, the language model is certainly capable of assembling text from different sources and getting an answer that tends to be mostly coherent, but that is exact

Yes, the language model is certainly capable of assembling text from different sources and getting an answer that tends to be mostly coherent, but that is exactly what LLMs are designed to do. To the LLM is doesn't actually matter that all of that content is from different sources, all that matters are the statistical relationships it has derived for the various tokens in your prompt. But those are not unique solutions, that are existing solutions that it has cobbled together. It's been demonstrated repeatedly that when prompted for information that ChatGPT has not been trained on, it fails badly. It also fails at a lot of higher order writing analysis prompts, like those found on English literature essay exams. I've been a software engineer for almost two decades, and I actually use ChatGPT daily. It's a very helpful reference, but fails early and often on a lot of tasks. I generally compare it to a tool like create-react-app, except generalized across all languages and frameworks. A very powerful tool, but it's a statistical machine learning algorithm, not an AI. It doesn't understand your prompts and it isn't reasoning about them.