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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

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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.

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