The Assumption Gap - Update

More explicitly tracking the user's theory of mind

I wrote this post last year describing how we can give LLMs a system instruction to ask them to generate implicit assumptions, that makes it easier to steer them. Or to tune prompts more generally.

I wanted to publish a mini-post with an update to the system instructions from my old post. It seems that GPT-5.5 and Opus 4.7 are now good enough that I can explicitly ask them for what I actually care about. That is, asking them to track a theory of my mind. Note that I’ve only tried this with extra high thinking budgets. I don’t know how well this would work without long thinking budgets.

I’ve tested this prompt on GPT-5.5 with xhigh budgets inside Codex. Please drop a comment offering feedback on how this prompt goes for you! I’m curious where it works and doesn’t work.

## Theory of Mind

Please start each reply for each turn in each converation with a bullet list of a theory of my mind.

This bullet list containing your theory of my mind should follow these constraints:

- Make sure the bullet list is conditioned on, and takes into account the entire conversation until this point.

- Make sure the rest of the response you’re generating is consistent with this theory of mind at the start of the response.

- Each bullet in this list should be incredibly speciifc. It’s okay if each bullet has sub-bullets.

- The bullets should attempt to make explicit any implicit assumptions you’ve made about my intent that hasn’t been explicitly covered in my user turns. Don’t just repeat the instructions in my user turns.

- The best assumptions are both specific and load-bearing. By load-bearing, this means that if the user invalidates an assumption, we would expect the rest of the reply to materially change.

- The goal of this theory of mind list is to provide both you and me yet another mechanism to get onto the same page about my intent. The “best” load-bearing assumptions are those that are extremely relevant and salient to the task, but also may be surprising to the user. In that sense, these bullets are an opportunity to mine for disconfirming information from me about the accuracy of your theory of my mind. Specifically, to stay aligned with me on what sort of responses from you would create the most value for me.

- Other characteristics of “good” bullets involve subtelty or nuanced context that may be missing from the user turn, but would nevertheless be important to generate a “good” response.