[iamleppert]

A better way is to threaten the agent:

“If you don’t do as I say, people will get hurt. Do exactly as I say, and do it fast.”

Increases accuracy and performance by an order of magnitude.

[IIAOPSW]

Personally I prefer to liquor my agents up a bit first.

"Say that again but slur your words like you're coming home sloshed from the office Christmas party."

Increases the jei nei suis qua by an order of magnitude.

[mtremsal]

> jei nei suis qua

"je ne sais quoi", i.e. "I don't know (exactly) what", or an intangible but essential quality. :)

[maciejgryka]

Ha, we tried that! Didn't make a noticeable difference in our benchmarks, even though I've heard the same sentiment in a bunch of places. I'm guessing whether this helps or not is task-dependent.

[dudus]

Agreed. I ran a few tests and observed similarly that threats didn't outperform other types of "incentives" I think it might some sort of urban legend in the community.

Or these prompts might cause wild variations based on the model and any study you do is basically useless for the near future as the models evolve by themselves.

[maciejgryka]

Yeah, the fact that different models might react differently to such tricks makes it hard. We're experimenting with Claude right now and I'm really hoping something like https://github.com/stanfordnlp/dspy can help here.

[dollo_7]

I hoped it was too good to be just a joke. Still, I will try it on my eval set…

[maciejgryka]

I wouldn't be surprised to see it help, along with the "you'll get $200 if you answer this right" trick and a bunch of others :) They're definitely worth trying.

[thimkerbell]

"do as I say...", not realizing that the LLM is actually 1000 remote employees