But that context is a mix of model output and other sources. The model output portion was generated token by token, and is combined in interesting ways with things like human response, search results, software output. It’s still a backward looking mechanism, rather than having established a concept as a goal and then trying to build the words to reach that concept like we do.

Size and strategy for managing the context has been critical for improved subjective results, but it still doesn’t exhibit the behavior of the words as a tool to address some concept, everything about the model is about the words themselves. So we end up with something very good at generating what seems right and there’s a super high chance of what seems to be right actually being right. Especially when the software can automatically execute commands and the good or bad results reach into the context window, enabling it to effectively get automatically second guessed. The potential for automatic verification in some scenarios automatically feeding the context window is what makes it particularly appealing for software folks, though not universal.