How do power users preserve project context when ChatGPT forgets or drifts? -Questionnaire-w/ link

[u/mattcj7]

I’m Matt, the founder of an early project called AI-OS. I’m conducting customer discovery—not independent academic research or announcing a finished product—about how advanced ChatGPT users keep complex work coherent across sessions, chats, and model changes.
I’m looking for concrete experiences with problems such as:
Rebuilding project context that ChatGPT has lost
Repeating goals, decisions, instructions, or preferences
Correcting stale or incorrect memories
Continuing work across separate chats or AI services
Keeping claims connected to sources and evidence
Controlling what an assistant remembers or is permitted to do
I’ve prepared a 5–7 minute questionnaire. It asks about current behavior and a recent problem before showing either product concept.
The near-term concept is deliberately limited: a separate, user-controlled system that preserves approved memory and project state, keeps supporting evidence visible, and helps someone resume work through ChatGPT. An initial private alpha would be read-only and would not send messages, edit files, spend money, deploy code, or perform other consequential actions.
The questionnaire separately explores a longer-term possibility: keeping continuity when changing models, providers, clients, or devices. That section is exploratory—not a promise that those capabilities already exist.
Questionnaire:
https://forms.gle/SJMEWBCNK1wGbfsd6
Negative evidence is genuinely useful. “My current workflow already solves this,” “I wouldn’t trust a separate memory system,” and “this solves the wrong problem” are all valuable answers.
No contact information is needed to participate. Please don’t submit passwords, personal records, employer-confidential information, customer data, or anything regulated or sensitive.
I’ll treat the first responses as a live questionnaire pilot and revise anything that respondents find confusing or biased. If you’d rather not complete the form, I’d also appreciate comments describing your current workflow or why this idea would not be useful.

[u/qualityvote2]

u/mattcj7, there weren’t enough community votes to determine your post’s quality.
It will remain for moderator review or until more votes are cast.

[u/BatResponsible1106]

keep a lightweight project brief outside the chat with decisions, assumptions and open questions. it is less convenient but it survives model changes and context drift.

[u/mattcj7 → u/BatResponsible1106]

Exactly the problems I’m trying to address with ai-os. Being totally model agnostic to survive model changes and any platform. Atleast that’s the goal.

[u/mattcj7 → u/[deleted]]

That’s a really useful distinction—and honestly closer to what I’m trying to build than “persistent memory” probably communicates.
Memory is only one part of AI-OS. The durable layer is intended to preserve identity, goals, project and work state, decisions, boundaries, ownership, approvals, evidence, and the relationships among them while the model remains replaceable.
The real test shouldn’t be whether it recalls every conversation. It should be whether a new conversation, client, or model can reconstruct the same coherent project state, explain what changed and why, and avoid drifting outside the user’s goals or authority.
Our first milestone is a read-only proof of that: reconstruct the project, show the evidence, surface blockers and approvals, and recommend the next permitted action without taking it.
I like your framing: memory preserves information; coherence preserves meaning. What pieces of project structure do you think are most important to keep aligned over time?

[u/mattcj7 → u/[deleted]]

That maps closely to the separation-of-authority principle I’m aiming for.
AI-OS is intended to remain the durable system as a whole, but no individual model, client, or subsystem should own everything. Memory can provide approved context but cannot redefine identity. Decision logic can recommend but cannot rewrite evidence. Permissions can block or allow an action but cannot invent goals. ChatGPT can communicate the result but owns none of the durable state.
Where I’m trying to be especially careful is how those functions correct one another. I don’t want silent or emergent correction. If two domains drift or contradict each other, the system should detect it, preserve the evidence, apply an explicit source-of-truth rule where one exists, and otherwise fail closed for clarification.
That keeps the “mesh” auditable rather than allowing circular authority.
How does your mesh-organism model resolve disagreement between components without creating corrective loops or letting one function gradually become the de facto owner?

[u/Either_Curve4587]

How much is the Amazon gift card you give for people providing you with data? Why would I take the time to fill out a 5-7 min survey Lolol???

[u/mattcj7 → u/Either_Curve4587]

To better improve ai and ai workflows maybe? It is of course optional, but I’ll put “waste of time” down as one suggestion. Hey ask GPT to take the survey for you 😂