That usually ends up being a poor use of LLMs, and is an unsolved problem with LLMs.
replyRAG was supposed to be the way out on that, and ended up being mostly abandoned.
That doesn't make much sense to me because this is in nature much like how harnesses operate: launch a bunch of exploratory subagents to search and retrieve evidence to use in the actual prompt. Think of it as caching this end result so you don't have to re-fetch in the codebase.
replyThat's the other way of doing it, which solves the context rot problem in a more complex way. The model at the top says, "hey, sub-agent, go figure out the answer to this question and give me the answer", and that sub-agent can go consume 250k+ context to return an answer that might be a couple of words, and thus not contaminate the main context with that now thrown-away context.
replyHowever, this is not something that is inherently part of models or inference engine, but part of the harness.
Harnesses are very hit and miss, and are not integrated into the stack, and I think that will have to happen eventually. Like, conceptually similar to an LLM performing a tool call that just calls itself recursively, I think this would go a long way to making LLMs more viable for being an actual product people could conceivably want.