It seems to write in the generic "style" of GPT, instead of in the style I would recognise as a HN poster. Is that because of something baked into how the training process works? It lacks a sort of casualness or air of superiority ;)
If he wanted it to replicate that classic HN feel he would either have to extend the prompt with additional examples or, better yet, use finetuning.
I guess he could also just randomly sprinkle in some terms like 'stochastic parrot' and find a way to shoehorn Tesla FSD into every conversation about AI.
First sentence of the first paragraph on OP's page
EDIT: it's a bit misleading, further down they describe what looks like a semantic-search approach
> 7. Put top matching content into a prompt and ask GPT-3 to summarize
> 8. Return summary along with direct links to comments back to Discord user
To be clear, I think this is still very cool, just misleading.
Build a model off of that?
https://sonnet.io/posts/emotive-conjugation/#:~:text=I%27m%2...
My conclusion was that you can use LLMs to automate and scale attribution bias.
We did it guys!
;)
> iii. Put top matching comments into a prompt and ask GPT-3 to answer the question using the context
It depends on the Prompt used to ask GPT the question. A prompt that instructs GPT to write like a HN poster should fix that.