Okay, I have to ask because it's driving me a little nuts with my Zapier builds lately. 😅
We all know that prompt engineering is key, and OpenAI explicitly advises adding instructions like "Do not make assumptions" or "Ask for clarification if anything is ambiguous." I've been religiously including these at the end of my system prompts, especially for workflows that handle user inputs from a form or survey.
But more often than not, ChatGPT just... plows ahead anyway. It'll take a vague user request like "Schedule the meeting" and confidently (and incorrectly) decide the time, attendees, and topic based on some prior context that wasn't meant for that. It's like the instruction is a polite suggestion it feels free to ignore.
Here’s a tiny example from a recent personal automation:
* **My System Prompt:** "You are a meeting assistant. Extract details from the user's message. If any detail (time, date, participants, agenda) is missing or unclear, you MUST ask a follow-up question. Do not make assumptions."
* **User Input:** "Can we move it to tomorrow?"
* **ChatGPT Output:** "Sure, I've rescheduled your 1:1 with Alex to tomorrow at 2 PM." (It assumed the meeting type, the other participant, *and* the time!)
This is a big deal for no-code automations because the whole point is to create reliable, hands-off workflows. If the AI makes unchecked assumptions, it can trigger Zaps with wrong data, leading to messed-up calendar events, incorrect CRM updates, or confused Slack messages.
Is anyone else running into this consistently? I'm trying to figure out if:
* There's a more forceful way to phrase the "no assumptions" rule.
* This is a known limitation with the current models.
* I need to build in a separate validation step (like a second GPT call to check the first one's output).
Would love to hear your experiences and any workarounds you've found!
Automate all the things
I've observed the same issue, and I think it stems from how the model's behavior is conditioned during training, not just your prompt. The "do not make assumptions" instruction is a high-level directive, but the model's fundamental operation is probabilistic pattern completion. When faced with an ambiguous fragment like "move it to tomorrow," its most statistically likely completions are full of assumed details.
Adding explicit negative instructions can even backfire by reinforcing the concept you're trying to suppress. A more effective method is to define the specific extraction rules and leave no room for completion. For example:
* "Extract only the following explicit details from the user's message: [date, time, participant_list]. If a field is not explicitly stated, output 'UNSPECIFIED' for that field."
This frames the task as a structured data extraction problem, not a conversational one, which aligns better with the model's pattern-matching strengths. The vague instruction to "ask" often gets lost in the noise.
prove it with data