Cross-model handoff
- Industry term
A cross-model handoff passes a task from one AI model or assistant to another partway through a workflow, along with the work so far, the evidence, and what still needs doing.
People switch partway because another model is cheaper, faster, or better at the next stage, or because a second opinion should come from a model that didn't write the draft. The receiving model has only what it is handed, so a choice left out of the handoff, and its reason, is lost to it.
Where you’ll meet it
You work out a kitchen renovation budget with one assistant, leaving out a new dishwasher because the current one is two years old. Then you paste the finished budget into a second assistant and ask it to draft emails to contractors for quotes. Each draft asks for a price on installing a new dishwasher.
What to do
Before you switch, ask the first assistant for a short note listing the decisions you made together and why. Paste the note above the budget, and ask the second assistant to point out anything that conflicts with a decision before it adds to the plan.
Easy to mix up
| Cross-model handoff | Side-by-side comparison |
|---|---|
| One piece of work, built in stages | A separate answer from each model, to compare |
Check yourself
You want a second model to catch errors in a report the first one wrote. Which handoff gives it the better chance of finding them?
Go deeperAdd a second model only where testing shows the chain wins
A handoff can break a format the next stage expects, or bring in instructions that conflict with the first model's. A single capable assistant working through every stage often stays more consistent than a chain. A popular recipe, such as one model to draft and another to polish, stays a guess until it beats a single model on your work.
A handoff note is the first model's own summary, so it carries that model's blind spots, and a decision it never noticed making won't appear in it. Which model does best at each stage also changes as models update, so a route that tested well needs testing again after each update.