How I keep project context when switching AI models

Switching AI models is easy. Bringing your project context along is the annoying part.

You explain what you’re building, who it’s for, and the decisions you’ve already made. Then you open another chat and explain it all again.

I’m KAT, co-founder of 123sudo. I work on product experience, creative direction, and growth, and I use our AI workspace, 9xchat, daily.

Here’s how I approach keeping project context useful across conversations and models.

Start with the project, not the entire conversation
A long chat contains more than useful context. It also contains abandoned ideas, temporary questions, and decisions that may no longer apply.

I find it more useful to think of context as a short, living project brief:

Project:
What are we building?
Audience:
Who is it for, and what problem are they facing?
Current goal:
What are we trying to accomplish right now?
Decisions:
What have we already agreed on?
Constraints:
What must the work respect or avoid?

You can use this structure in a note and bring the relevant parts into whichever AI tool you use.

You can use this structure in a note and bring the relevant parts into whichever AI tool you use.

Separate lasting context from temporary instructions
Not every detail belongs in long-term memory.

A product’s audience or approved name may matter across many conversations. A request to “give me three headline options” usually matters only for the current task.

My rule of thumb:

• Lasting context: project facts, preferences, constraints, and confirmed decisions.

• Temporary instructions : the current output format, experiment, or question.
  That distinction helps keep a one-off request from becoming an assumption in future work.

Keep context available when changing models
This is one reason I use 9xchat, which brings multiple AI models and persistent memory into one workspace.

The aim is to keep useful context across models instead of manually rebuilding it in disconnected chats.

But memory is not a substitute for a clear request. I still need to explain what I want to do next and point out when a previous decision has changed.

Remembering the project and understanding today’s task are two different things.

Treat old context as something to review
Persistent context can be helpful, but outdated context can steer an answer in the wrong direction.

Before an important task, it’s worth checking:

• Is the audience still the same?
• Has the product or offer changed?
• Are earlier constraints still relevant?
• Are we exploring an idea or working from a confirmed decision?

When an answer feels off, the problem may be the context, not just the model.

Be selective about what you share

A project brief doesn’t need every document or personal detail.

Include what the task needs. Leave out sensitive information that isn’t necessary, and check the tool and model’s data-handling settings before sharing confidential material.

More context isn’t automatically better context.

My takeaway

The useful habit isn’t saving every conversation. It’s maintaining a clear picture of the project: what matters, what changed, and what comes next.

Whether you use a shared note or an AI workspace with persistent memory, that habit gives you a more consistent starting point when switching models.

Disclosure: I’m a co-founder of 123sudo, the team behind 9xchat. AI helped draft and edit this article; I reviewed the final version.

How do you carry project context between AI models: a project brief, saved notes, persistent memory, or something else?

Disclosure: I’m a co-founder of 123sudo, the team behind 9xchat. AI helped draft and edit this article; I reviewed the final version.

How do you carry project context between AI models: a project brief, saved notes, persistent memory, or something else?

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