[speedgoose]

It shouldn't be too hard to filter such prompt engineering hacks from the futur training datasets.

[catchnear4321]

a lot of things shouldn’t be hard.

a lot of things are harder than they look.

[speedgoose]

True. But this one I’m very confident I can do it myself and I’m not even an expert in the field.

[catchnear4321]

ok then. do it and post it to HN. put it up for scrutiny and testing.

i’m very confident someone can prove you wrong, without being an expert in the field.

[speedgoose]

I would start by creating a dataset of such prompt hacks. A lot of them are already on GitHub, Reddit, and HN.

To get even more of them I could consider gamification. This game is a good example: https://gandalf.lakera.ai/

Once I get a descent dataset, I could use it to finetune a LLM to do classification. Or play with embeddings and cosine similarity and similar.

I could also use LLMs to extend the training dataset, and have some human feedback.

It’s maybe not the best strategy and I’m sure someone else can do it better but I don’t think it’s wrong.

[catchnear4321]

so, to summarize, you think it is easy, and you think you have an approach that would lead to a viable solution.

while interesting, your napkin math isn’t convincing.

[speedgoose]

I’m sorry I didn’t convince you.