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Unfortunately people sometimes get defensive against this take. But I think treating the LLM as you described can make you a better LLM user and help get better output. It helps understand the failure modes better, and moderate one’s reliance on them. Just like how we should do for every tool we work with.

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Yes, I've found that reminding yourself of how they actually work helps keep you on guard against LLM-patterned mistakes. Especially things like carefully considering what parts of the current task likely fall outside the distribution of corpus + RL data (as much as that can be guessed).

> ...Unfortunately people sometimes get defensive against this take. But I think treating the LLM as you described can make you a better LLM user and help get better output. It helps understand the failure modes better, and moderate one’s reliance on them. Just like how we should do for every tool we work with.

B...b...but the Anthropic trainer said we'd get the best results if we don't think of it as a tool, but instead give it a name and think of it as our brilliant coworker!

Why should I trust you, internet rando over a stormtrooper-level salesman? /s

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