Sander Schulhoff

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947 appearances 1 recordings 1 series first heard Jun 2025 last heard Jun 2025

Sander Schulhoff’s voice in public audio — every appearance, attributed to the second.

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But at the beginning of my process I was getting little to no accuracy.
Uh it wasn't outputting the codes in a a properly formatted way.
Uh it wasn't really thinking through well uh
how to code the document.
And so what I ended up doing uh was taking uh kind of a
a long list of documents that I went and coded myself or I guess got coded uh and I took those uh and I attached kind of reasonings as to why uh each one was coded in the way it was.
Uh and then I took all of that data and dropped it into my prompt uh and then went ahead and gave the model like a new transcript it had never seen before uh and that boosted the accuracy on that task up by I think like 70%.
So massive, massive performance improvements by having better prompts and doing
was uh actually a bit of a recent development for me uh in terms of kind of thinking through this and explaining it to folks.
But the two modes are
Uh first of all, there's the the conversational mode uh in which most people do prompt engineering.
And that is just you're using Claude, you're using Chat GBT, you say, hey, you know, can you write me this email?
It does kind of a poor job, and you're like, oh no, like make it more formal or add a joke in there, and it adapts its output accordingly.
Uh and so I refer to that as conversational prompt engineering because you're getting
it to improve its output over the course of a conversation.
Uh notably, that is not where the the classical concept of prompt engineering came from.
Uh it actually came uh a bit earlier from a more
I guess AI engineer perspective, where you're like, I have this product I'm building.
I have this one prompt or a couple different prompts that are super critical to this product.
I'm running like thousands, millions of inputs through this prompt each day.
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