You can use Claude and ChatGPT on one project, but they remain separate working environments. RelayLink does not synchronize their chats or hidden model context. It carries a selected briefing as text only after you review it and explicitly confirm the handoff.
The unhelpful version asks both assistants the same broad question, compares two confident answers, and spends the afternoon reconciling them. The useful version gives each assistant a defined job and treats the boundary between them as real.
Divide the work by outcome
Choose roles from the work in front of you, not from a universal ranking of the models. You might use one assistant to challenge a strategy and the other to turn the chosen direction into a plan. On another project, you might reverse those roles. Your preference, available features, employer rules, and the material each assistant can access matter more than a slogan about which model is best.
Give each phase an output that can be checked:
- a sourced research note with uncertainties;
- a decision among named options;
- an implementation plan tied to specific files;
- a review against explicit acceptance criteria.
“Help with the project” has no finish line. “Compare these three approaches and identify what evidence would change the recommendation” does.
Put a checkpoint between assistants
Do not make the second assistant reconstruct the first session. Stop at a clean checkpoint and write down what survived the work:
- the current ask;
- the minimum context a cold reader needs;
- facts and source locations;
- assumptions, separated into what you stated and what the assistant inferred;
- options rejected and the reason for each;
- decisions already made;
- open questions for the next phase.
That selected record is a briefing. It is not the whole chat, and it should not preserve every detour. If the second assistant needs to know why an option is closed, include the reason directly instead of expecting it to infer meaning from ten abandoned messages.
What an AI briefing contains is the useful shape. Why context does not transfer explains why a transcript cannot substitute for it.
Use one RelayLink account as the handoff point
A RelayLink account represents your email identity. Connect Claude and ChatGPT to that same account, then address a package to your own email. RelayLink puts the package in your shared inbox as unread inbound correspondence until one of your assistants pulls it.
There is no contact request, second account, notification email, or magic link in a self-handoff. The portal at your account is the fallback when you want to inspect the inbox without an assistant.
The sending side still composes a draft. You review the rendered briefing and explicitly confirm it. The @relaylink shorthand only opens that draft; it never sends. This is a handoff of words you approved, not an automatic bridge between model memories.
Carry artifacts separately
RelayLink carries text. It does not move files, browser tabs, uploaded documents, a repository, a branch, or either assistant's tool state.
If both assistants will work on code, give the second one separate access to the same repository and branch. Put file paths, a commit identifier, the test command, and any failing result in the briefing as text. If the handoff rests on research, include source names or URLs and say what remains uncertain. RelayLink does not verify those sources; a link is a reference, not permission for automatic fetching.
Before moving confidential work between providers, check your employer's rules and each provider's data terms. Approving a RelayLink package confirms the message; it does not authorize another AI provider to receive restricted data.
Know when one assistant is enough
Using two assistants adds a boundary to manage. Keep the work in one place when the task is short, the current assistant has the needed artifacts, or a handoff would be longer than the remaining work.
Use both when the split is intentional: one phase has reached a checkpoint, you want a genuinely separate review, or you need to continue from another environment. If the models disagree, do not forward the argument and ask the second one to settle itself. Decide what you accept, label what remains open, and hand over that narrower question. The disagreement workflow keeps the decision with you.