[tobiadefami]

Thanks for taking the time to try it out and share your thoughts. I really appreciate the detailed feedback from a real-world use case.

Glad to hear the setup was smooth and that the chat box + import/export features worked well for you. Noted on the UI tweaks, I'll look into making them more intuitive.

On the categorization issue, yeah, LLMs can struggle with nuanced transaction labeling, especially without proper context or examples. Structured prompting could help, which ties into your second point -- having a library of refined prompts that can be reused for repetitive tasks would be really valuable.

I love your feedback -- it's exactly what helps improve the tool. And again, thanks for testing it out!

[tobiadefami]

Prompt library has also been implemented and can be opened with ctrl+shift+L; or cmd+shift+L on mac

[librasteve]

having thought about this a bit - my new conjecture is that if I had a way to feed in an example map of transaction payee => category, as one of the prompts, and a way to incrementally add prompts for outliers, then the AI _might_ be able to do a reasonable job - I am planning to mess with raku LLM::Functions to see if I can get this to work

[tim-fan]

Hi I've been thinking about the same thing, in the context of beancount / plain text accounting.

https://www.reddit.com/r/plaintextaccounting/s/BKsaLrfy3A

I already have thousands of labeled examples and a list of valid categories. I'm also hoping an llm will do a reasonable job.

At the moment I'm wondering what to do with all the example transaction data, as it's likely larger than the context window. I guess I could take a random downsample, but perhaps there's a more effective way to summarize it.