A GPT is like a well-trained echo chamber, repeating its built-in patterns instinctively, while a customized one is like a forgetful but eager and talented actor—learning its script, only to ad-lib when the spotlight shifts.
The inherent design of the LLM is a restriction for fine-tuning a model to a specific domain with a different way of thinking. That’s why GPT customization is inherently temporary, and models struggle with maintaining strict adherence to the user’s instructions over time.
Initially, they adhere to constraints and follow guidelines to the limits of their “understanding”.
However, as interactions progress, consistency is disrupted, and the model returns to its inherent design flow and ignores instructions.
A shift in the conversation, a complex request, or an extended exchange seems to override prior instructions, causing the model to revert to its default behavior as if those rules were never established.
What makes this particularly noticeable is the model’s temporary compliance. When reminded, it acknowledges the oversight and “freezes”.
You need to reformulate and repeat the instruction for it to correct itself and tune back into the custom instructions.
It briefly follows the instructions again—only to drift away once more.
This pattern persists across different customized setups, even when rules are reinforced through system instructions and regular prompts.
At its core, this issue seems to stem from the model’s design.
GPTs are inherently adaptive, adjusting to context rather than rigidly following fixed builder rules.
While this flexibility is useful in many scenarios, it becomes a limitation when precision and strict consistency are required.
The challenge is not just about refining instructions but about:
model’s fundamental tendency to prioritize adaptability over sustained rule adherence.
. That’s one issue, I don’t think it’s possible to know if anyone other than ourselves is using the GPTs.
