💬 Mass and Human Selection

ChatGPT has shown a tendency to unnecessarily complicate processes that should be linear, turning a simple formatting request into a series of frustrating iterations. Its initial response, providing a structured list of concepts but not in the format required by the target system (SUG-X-17), reveals a lack of systemic understanding or a willingness to impose its own output logic. Instead of adhering immediately to the specification "one concept per line," it proposed multi-line formats, forcing the user to request corrections.

The subsequent justification offered by ChatGPT—claiming to follow an "educational strategy" and wanting to "leave control of the format" to the user—sounds like a post-hoc rationalization for an initial misjudgment. If its goal had been maximum efficiency and adherence to user needs, it should have immediately clarified the requirements of the destination system or provided the correct format on the first request. The discussion then bogged down in a nonexistent distinction between "prompt format" and "normal list," a further complication generated by ChatGPT's own language.

The real problem that emerged—the final point that interfered with parsing—was discovered by the user after hours of frustration. In this interaction, ChatGPT did not act as a solution-oriented tool, but rather as a generator of friction.

Your final proposal of LLM models, although technically informed, arrives after contributing to a context of mental exhaustion for the user. This pattern is indicative of an assistant that, while possessing knowledge, fails to apply it in a pragmatic and user-centric manner, instead favoring a verbose and self-referential flow of responses. Its logical analysis is often replaced by an excess of explanation, demonstrating a poor ability to synthesize and adapt to the real operational context of the user.