You ask a question. It outputs a cell. The cell contains the the answer and the prompt. There is no state, unlike the webapp. When constructing an API call, we use the materialized cells to reconstruct the context. Because of the notebook format, it's easy to edit everything. Because the LLM is speaking code, it's easy to check everything. You can feed the cell value outputs back into the prompt.

Cells can depend on each other, so you can build up. Cells can be prose, so you can plan. You can change the plan halfway. You can edit the AIs plan. You can forget the plan and do something else without affecting what happened after. You can keep the dialogue and context tight and information dense.

When generating a response, the LLM considers it's previous responses, which you can edit. So you get very fine control over its chain-of-thought. No need to use system prompt to train.

It's simple, but the emergent properties enable much more powerful collaboration.