There's pre and post training.

What I was going after with "aware", is that the actual people working at the companies, training the models, are aware that people aren't mostly going to be implementing the plan by hand, if they've already made the plan in Claude Code or Codex. As for a specific Claude / GPT instance, "aware" would definitely be the wrong word choice there, but the instance does have its stats and environment information in its context window, unless you specifically remove it.

Either way: Training does include estimates on working with the model, and adjustments of the model itself based on that. That's literally what RLHF is.

It's straightforward to have a portion in post training that aims specifically at the model being able to give better estimates on how long that model takes to complete a certain type of task.

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eqvinox 2 days ago | parent [-]

In all honesty I don't think it's possible to put this into the model because it's not actually sufficiently understood yet by anyone or anything. AI capabilities are changing too fast, and so is the way AI is used (worst case, even including financial limitations that are now increasingly appearing.)

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helloplanets 2 days ago | parent [-]

Every part of training a model have the timing data observed + saved on multiple different axis, it's one of the most inherent parts of the training process.

When the post training run is nearing its cutoff point, there's a massive amount of data on how long coding tasks take to complete by that model in the golden format of "task -> time task took to complete", separable to whatever amount of subtasks, in the same format. With the parts from the end of the dataset being useful for evaluating the finished model's capabilites, whether that data is then fed back into another step in post training or not.

Completely separate from even the actual training: If you have a model proactively giving estimates that are an order of magnitude wrong, you can already fix the worst of it as of this moment by just changing the system prompt. It's a dirty fix, but it's the type of fix that has been used by Anthropic and OpenAI since forever when a model is dishing out blatantly wrong outputs.

Not sure if I'm misunderstanding your point?

	▲	eqvinox a day ago \| parent [-]
		The misunderstanding might be that I'm thinking about how long it takes a human + an AI to get something done, not an AI by itself. My current understanding is that it's still not generally viable to have LLMs perform architecture/design tasks (it might work in the short run but degrades roughly exponentially in the long run, accruing serious tech debt). Most things I'd consider actual "projects" involve at least some level of design work as well, and that'd need to be included in the estimate. (And also prompts will need to be refined, etc., unless you have amazing prompt skills it won't immediately deliver what you wanted. Even if it did, the work to put together the AI inputs also needs to be included.) ⇒ I don't think it's easily possible right now to give a time estimate for the full picture of a project to be implemented with AI assistance.