The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Usama Shafqat no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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1exchanges match
1on raw tape
0redirected or not addressed
Partly raw tape D 3 · C 3 · P 2 · Cm 2 2.60

Q Have you found differences in having one model just generate the conversation and then using text-to-speech to kind of fake two people? Or, like, are you actually using two different kind of system prompts to, like, have a conversation step by step? I'm always curious, like, if persona system prompts make a big difference or, like, you just put in one prompt and then you just let it run?

A I guess, like, generally we Use a lot of inference, as you can tell with like the, the spinning thing takes, takes a while. Um, so yeah, there's definitely like a bunch of different things happening under the hood. We've tried both approaches and they have their sort of drawbacks and benefits. I think that that idea of like questioning, like the two different personas, like persists throughout, like whatever approach we try, it's like, there's a bit of like imperfection in there. Uh, like we had to really lean into the fact that like, To build something that's engaging, like it needs to be somewhat human and it needs to be just not a chatbot. Like that was sort of like what we need to diverge from is like, you know, most chatbots will just narrate the same kind of answer, like given the same sources for the most part, which is ridiculous. So yeah, there's like experimentation there under the hood, like with the model to like, make sure that it's spitting out like different takes and different personas and different sort of prompting each other is like a good analogy, I guess.

AI assessment note: “We've tried both approaches and they have their sort of drawbacks and benefits.”

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