In my experience, architecture stays. A nice modular codebase (where you can switch-out the various models and have control on the minutia of the pipeline from data to model) brings a lot of value and can fairly easily be kept up-to-date with best practices.
(author) It's very similar. I think the extension of this is that we could build a frontend gui for something like this for prompt management. Love what they're doing
You should add a very short description of what this is and how it works. I kind of get that it is a way to tune/generate prompts for specific input output test cases, But its not clear exactly what it does.
Sorry to ask, but would you mind expanding a little more?
For example, when you state "our system iteratively rewrites the prompt to better fit the desired behavior", what is the use case for this?
Is it to make personal datasets? Thank you.
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[ 27.6 ms ] story [ 341 ms ] threadLLMs come and go.
Prompt engineering techniques come and go.
But eval / labelled dataset is always useful once you built it.
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
https://github.com/hitchdev/hitchstory/blob/master/examples%...
Seems like an awesome idea. I'm curious how long it will take to get a model on a reasonable level.
Phind is pretty good and also the fastest model I used recently, so I'd assume it's quite small, no?