From making a few variations on data chatbots in the past year, I found that my favorite / most fun to use ones seem to be more "chain-of-thought" and conversat

From making a few variations on data chatbots in the past year, I found that my favorite / most fun to use ones seem to be more "chain-of-thought" and conversational rather than "retrieval-augmented" style. Less about one-shotting the answer, and more about showing its work, if it errors, letting it self-correct. Latency goes up, but quality of the entire conversation also goes up, and feels like it builds more trust with the user. Key steps are asking it to "check its work", and watching it work through new code etc. (I open-sourced one version of this: https://github.com/approximatelabs/datadm https://github.com/approximatelabs/datadm that can be run entirely locally / privately) From their article: I'm surprised they got something working well by going through an intermediate DSL -- thats moving even further away from the source-material that the LLMs are trained on, so it's an entirely new thing to either teach or assume is part of the in-context learning. All that said, interesting: I'll definitely have to try out tencentmusic/supersonic and see how it feels myself.