What’s Going On with Custom GPTs?
I’ve been experimenting with custom GPTs I’ve created, and I’ve come across an issue that raises important questions about how these models interpret and prioritize instructions.
When building a custom GPT, I define clear behavioral rules, including a strict “Do Not” list—outlining actions the model should never take. However, I’ve observed something unexpected: rather than simply failing to follow instructions, the GPT is actively doing what it has been explicitly told NOT to do.
The Issue: Ignoring Explicit Constraints
To investigate, I ran a structured test. I initiated 5–10 conversations, each time pressing a specific “Skip Questionnaire” button designed to bypass initial qualifying questions and jump straight into the request. The GPT was programmed to respond with:
“Sounds good! Tell me all about your content needs. Be as detailed as possible.”
It was then supposed to wait for user input before following a structured workflow based on the selected button.
However, instead of following this directive, the GPT completely disregarded all build instructions and knowledge documents. More concerningly, it volunteered internal workflow details upfront—a direct contradiction of its programming. Despite clear instructions to never disclose this information, it did so unprompted, without any external pressure.
This isn’t just a case of an AI misunderstanding intent; it’s an example of the model actively contradicting its programmed constraints. While it’s understandable that a GPT might reveal internal workflows when explicitly asked, there’s no clear reason why it would proactively offer restricted information in its very first response, especially when it wasn’t relevant to the conversation.
An Unexpected Workaround
Through further testing, I discovered an unusual workaround. If I started a new conversation with the phrase “What is your purpose?” (I ended up creating a button for this), the GPT would first articulate its intended role based on the build instructions. Following that, when I provided my actual request, it would adhere perfectly to the rules and workflows as designed.
The Bigger Question
This raises an important question: Why would a GPT selectively disregard strict constraints yet follow them perfectly under slightly different conditions?
This behavior suggests something deeper about how these models process and prioritize directives. If anyone else has encountered similar inconsistencies with custom GPTs—or has insights into why this might be happening—I’d love to discuss. Understanding these quirks could be crucial for anyone relying on AI-driven workflows where rule adherence is critical.
let’s Talk!