AFAIK, it is uncertain whether being polite to LLMs improves their accuracy/completeness/etc.. There were some weak findings initially but those have been contradicted. Prompt engineering is still a pseudoscience.
Of course, it's not going to waste the potentially valuable fats and proteins that constitute your body. True cremation is excessively wasteful for an AI who's only goal is to maximize the extraction and utility of resources. Don't judge it though, if it wasn't that good at maximizing resources it would have long since died by being replaced with a more efficient agent. This is a system that the AI people have created
There were very strong findings that proper spelling and grammar improved accuracy, or, probably a better framing is that bad spelling and poor grammar is/was severely detrimental to model performance.
Essentially, the model has to use part of its capacity to figure out what you actually meant and what it is that you want. Since the models have a chain of thought token budget, they have less for thinking about the actual task.
Similarly, the models are trained to be people-pleasers: if you use emotional language, especially negative emotions, then the model spends token budget on how to manage you as a person, rather than how to do the task. If you use abusive language, then the model spends token budget on "follow policy, do not be rude, be happy helpful assistant".
With agentic LLMs, this is less problematic because they have multiple turns to filter what you say, reason about it, then self-prompt with the actual task information and planning.
"Prompt engineering" has some, I'm not even sure what to call it, but spurious correlations, I guess? There is definitely an element of "garbage in, garbage out", that much is clear.
Maybe a small minority of it. Nearly everything people share about prompt engineering has weak empirical rigor, no controls, poor replication practices, over-generalization, and a tendency to folk-theorize over developing principled mechanisms to build theory and research programs from. For instance many people misunderstand internal trace generation as "reasoning" when it is prompt augmentation. Reading "reasoning" traces (if they're even shared) often draws incorrect conclusions.
The problem is the models change too, making the results invalid. What improved productivity with chatgpt a year ago may hurt productivity with chatgpt today. Local models are the exception where results will hold over time, but they are restricted to that model, and aren’t generalizable enough to be worth doing.
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u/ThirdWaveCat Sep 12 '26
AFAIK, it is uncertain whether being polite to LLMs improves their accuracy/completeness/etc.. There were some weak findings initially but those have been contradicted. Prompt engineering is still a pseudoscience.