How to Keep Your Context Across ChatGPT, Claude, and Every AI

Using more than one AI is normal now.

You might use ChatGPT to think through an idea, Claude to write, Perplexity to research, Grok for a quick take, Manus for agentic work, and Mistral to test another model.

The problem is what happens every time you switch.

Blank slate.

You explain the project again. You paste the background again. You correct the tone again. The AI tools are getting better, but your context is still scattered across chats, apps, custom instructions, and old notes.

This is not really a prompting problem. It is a memory problem.

Here are the practical ways to keep context when moving between AI tools, from manual workarounds to the cleaner long-term answer.

1. Keep a short context card

The fastest fix is a short note you paste into every new AI chat.

Keep it under 200 words. Include:

• Who you are
• What you are working on
• Your preferred tone or answer style
• The constraints that matter
• Anything the AI should avoid

For example:

I am building a portable AI memory product. Write in a clear, direct founder voice. Avoid hype, vague productivity language, and long intros. Assume the audience uses ChatGPT, Claude, Perplexity, and other AI tools for real work.

That one paragraph will usually improve the first answer more than a clever prompt.

Why it breaks: you have to paste it every time, and it gets stale quickly.

2. Keep project briefs separate

Your personal context is not the same as your project context.

If you use AI for multiple things, create one brief per project: startup, newsletter, client, roadmap, research, whatever you come back to often.

A good project brief answers:

• What is this project?
• Who is it for?
• What has already been decided?
• What are the current goals?
• What constraints should not be violated?

When you move from ChatGPT to Claude, paste only the relevant brief. The next AI starts closer to the truth.

Why it breaks: the brief is only as current as your last update. The second your project changes, the brief starts drifting away from reality.

3. End useful chats with a handoff note

The easiest way to keep your context updated is to make the AI summarize it for you.

Summarize the durable context from this chat: project facts, decisions made, constraints, preferences, and next steps. Leave out temporary discussion.

Then paste that summary into your project brief.

The word durable matters. You do not need a transcript. You need the facts and decisions that should still matter tomorrow.

Why it breaks: it still depends on you copying the note into the right place. If you forget, the context stays trapped in the old chat.

4. Use custom instructions and built-in memory

Custom instructions are worth setting up in every AI tool you use. Put your baseline preferences there: how direct you want answers, how much detail you like, and what the assistant should avoid.

Built-in memory is useful too. If ChatGPT remembers your tone or Claude remembers a project preference, the product gets better.

Use both.

Why it breaks: they are local. ChatGPT's memory helps inside ChatGPT. Claude's memory helps inside Claude. A useful thread in Perplexity does not update Manus. Each tool still has its own version of you.

The real problem: you become the sync layer

None of these methods are bad. They are the right starting point.

They just make you responsible for syncing memory across every AI.

That does not scale.

If you switch between assistants every day, ChatGPT knows one version of your project. Claude knows another. Perplexity has last week's research. Grok has no background. Manus is working from whatever you remembered to paste.

The question stops being "What prompt should I use?"

It becomes "Why is my AI memory still manual?"

The cleaner answer: one shared memory

Instead of trapping context inside one assistant, your memory should sit outside the assistants you use.

That is why we are building Tabula: one shared memory across the AI tools you connect.

Save a durable fact, preference, decision, or project detail once, and another connected AI can recall it when it matters.

The assistants do not need to talk to each other. They use the same memory layer.

The important part is control. You can read what is saved, edit what changed, delete what should not be there, export it, or pause access for a connected AI.

The manual system helps you decide what context matters. Tabula handles the part you should not have to keep doing: moving that memory from one AI to the next.

Try the two-minute version

Connect Tabula to two AI tools from the setup guide.

Tell the first AI one durable fact about your work, your preferences, or a current project. Then open the second AI and ask something that needs that fact.

If the second AI can use the context without you pasting it again, the value becomes obvious.

You stop treating every new chat like onboarding. You stop starting from zero.

Your context should not reset every time you switch to another AI.

One memory. Every AI.