Reconstruction of reasoning from scratch can happen in some legacy APIs like the OpenAI chat completions API, which doesn't support passing reasoning blocks around. They specifically recommend folks to use their newer esponses API to improve both accuracy and latency (reusing existing reasoning).

A simple example: can you arbitrarily manipulate the historical context of a given request to the LLM? It's useful to do that sometimes. Another one: can you create a programmatic flow that tries 3 different LLM requests, then uses an LLM judge to contrast and combine into a best final answer? Sure, you could write a prompt that says do that, but that won't yield equivalent results.

These are just examples, the point is you don't get fine control.

I wonder if this has any real benefits over just doing very simple html wireframing with highly constrained css, which is readily renderable for human consumption. I guess pure text makes it easier to ignore many stylistic factors as they are harder to represent if not impossible. But I'm sure that LLMs have a lot more training data on html/css, and I'd expect them to easily follow instructions to produce html/css for a mockup/wireframe.