Hi, I briefly scanned through the article.
So in the essence you give LLM a bunch some CSV example and then ask LLM to write a python code, which would categorize similar, based on the keywords. So, this is a kind of alternative to teaching ML model based on the previous transactions to be able to categorize new ones. Did it get the main idea correctly? On Monday, March 11, 2024 at 12:36:00 AM UTC+1 [email protected] wrote: > Made a quick prompt over the weekend: > https://gist.github.com/jaanli/1f735ce0ddec4aa4d1fccb4535f3843f > > Results are that my partner (someone non-technical, design background, but > familiar with prompt engineering) can use the prompts—the last thing I > would want is an inscrutable system that I manually built to import > transactions from our dozen institutions across multiple countries & > currencies, that they can't re-use or extend. > > Visual Studio Code and the Beancount extension are already a stretch for > them so having something that works with a single prompt at a time and copy > and pasting was my goal. > > Hope this helps someone else! Surprised that these tools are not easier to > use (and thank you for beancount, this wouldn't be possible otherwise :) > > Would be fun to extend this with DSPy ( > https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb) which could > likely help squeeze several different converters into a few signatures > (compressed prompts), and things like chain-of-thought prompting (iterative > runs of large language models) would further reduce the > extract-transform-load overhead that has kept me from trying beancount all > these years. > > Very best, > Jaan > -- You received this message because you are subscribed to the Google Groups "Beancount" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/beancount/eefe5ba4-c0cd-46cd-9fb8-8922b399c221n%40googlegroups.com.
