This resonates. I build products on top of LLMs, and the most interesting work I do has nothing to do with AI; it's designing structured methodologies, figuring out what data to feed in before a conversation starts, deciding what to do when the model gives a weak answer. The AI is plumbing.

But nobody wants to hear about prompt calibration or pipeline architecture. They want to hear "I replaced my whole team with agents." The boring, useful work is invisible, and the flashy stuff gets all the oxygen

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selimthegrim 3 hours ago | parent [-]

Now do it with knowledge or causal graphs

	▲	buzarchitect 3 hours ago \| parent [-]
		Causal graphs are interesting, but in my experience, the bottleneck isn't the representation; it's getting the model to actually follow through on weak signals instead of moving on to the next topic. A graph won't help if the system doesn't know what to do when it hits a node that doesn't resolve cleanly. What's your experience been with them?