How do I stop losing context when I switch between ChatGPT and Claude?
Keep the durable part outside both of them. Your company's AI memory should belong to your company, not to one model. Once the rules, corrections, and reusable steps live in a layer of your own, each AI can receive the part that applies to the task, and switching products stops meaning starting over.
Each product keeps its memory inside itself
ChatGPT and Claude keep their native memory inside their own products. They do not continuously share that memory with each other today. If you want the same durable company context available across both, that context needs to live outside either product.
Meanwhile people use both on purpose. One model writes better, another handles long documents better, a third is what the engineering team lives in all day. Using the best tool for each part of the job means paying the setup cost again for each one.
Four ways people bridge it by hand
A context block you paste
A paragraph or a page kept in a note, pasted at the start of a session. It works, and it is where most people are.
One portable file
A Markdown file you point every AI at. Better, because there is one copy, and it is still a file that says nothing about who else may read it.
The same instructions, twice
Custom instructions in one product, a project file in another, kept in step manually until the day they are not.
An export and import tool
Move a transcript or a memory dump from one product to another. Useful once. It copies history rather than maintaining rules.
Where copying by hand is fine
For one person with a handful of stable preferences, a pasted block is genuinely enough. It takes ten seconds, it is transparent, and there is nothing to maintain but one note.
It holds up as long as there is one copy, one reader, and no question about who else should receive it.
Three failures that show up as soon as there are two copies
The copies drift
You improve the version in one product because that is where you were working. The other one keeps the old rule, and now two AIs give confidently different answers.
A correction lands in one place only
The fix you made in the middle of a coding session is not in the note you paste into the other product, so the same mistake is waiting for you there.
A pasted block cannot express an audience
It has no way to say this rule is for the revenue team, this one is confidential, this one applies only to new business. So the block stays generic and safe.
One layer of durable context, read by whichever AI is doing the work
The rules, corrections, facts, and reusable steps live in HexaHQ. Your AI reaches them from ChatGPT, Codex, Claude Chat, Claude Code, or Claude Cowork, and receives the part that applies to the task in front of it. Change a rule once and the next task in any of them starts from the new version.
Be clear about what does and does not move. This is not a sync of your conversations. Nothing inside either model is transferred, no chat history is copied, and no hidden state changes hands. What moves is the material you saved on purpose, which is also the only part worth carrying.
That is the ownership argument in practice: if your organization can change which AI it uses without rebuilding what it knows, the accumulated context is yours.
Portable does not mean public
Because the context lives in one layer rather than in each product, it can carry an audience. A rule can be personal, go to a named group, go to the whole organization, or go to a single external collaborator, and a person's AI uses only what that person is meant to have. Model neutrality and selective access are the same feature seen from two sides.
A rule learned in Claude Code, applied later in ChatGPT
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The rule is learned during real work
An engineer is writing a release note in Claude Code and corrects how the company names breaking changes for customers.
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It is saved once
HexaHQ keeps the corrected naming rule. The engineer reviews it and shares it with the product and support teams.
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A different person, a different AI
Days later a product marketer is drafting the customer announcement in ChatGPT. The naming rule arrives with that task.
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The work is completed in the real system
The announcement is written to the correct standard, and the release entry is updated in Notion after the marketer approves the change. Nothing was copied between the two AI products.
Questions people ask next
Will my ChatGPT memory follow me into Claude?
No. Each vendor's memory is tied to an account inside that product, with no route into a competitor's. What can be shared is a layer outside both, holding the rules and reusable steps each one reads when a task needs them. There is a longer answer on whether ChatGPT and Claude can share the same memory.
Which AI apps can use the same HexaHQ context?
ChatGPT, Codex, Claude Chat, Claude Code, and Claude Cowork today.
Do I have to commit to one AI vendor?
No, and that is the point. Keeping the durable context in a layer you control means changing which model you reason with does not cost you what your organization has learned.
Does this copy my conversations between AI products?
No. Nothing inside a model moves and no transcripts are copied. Only the material you deliberately saved is available to the next task.
What happens to context I already have in one product?
It stays where it is. The practical move is to save the rules you keep re-explaining as you next hit them, rather than trying to migrate a history.
Is a shared context layer slower than native memory?
It is a different trade. Native memory is closer to the model. A shared layer can hand over a short, current rule instead of long source documents, which is usually less to read, not more.
Related pages
Your company's AI memory should belong to your company, not to one model.
Keep the durable context in one place and use whichever AI suits the job. Free to start.