Comment by Twirrim
14 hours ago
The frustrating part about an LLM is at a fundamental level it's designed to do the wrong thing, it's designed to provide an answer for things it can't do, e.g. one argument I've been in recently at work is someone using it to summarise numerical data. They've a raw data spreadsheet they're putting into ChatGPT and asking it to produce a report in a particular style. So the LLM is doing its job, predicting the tokens to emit, and not actually doing mathematical analysis of the underlying data.
I had a team proudly show me how they were feeding their dashboard data into an LLM to predict growth needs, instead of doing basic linear regression or any number of pre-existing basic statistical analysis. The part that depressed me the most on that was it was a bunch of engineers that did it, the people you'd most hope would understand what an LLM is/does.
For numerical work, there’s no reason to assume the computation is happening in the LLM. All frontier models could handily write a Python script to do linear regression then report the result, so the quality of the responses here depends entirely on the prompt + harness.
+1. I noticed ChatGPT now pulls out a calculator or writes a script instead of hallucinating numbers. Initially it was terrible, but recently it seems pretty good... that said, if you're doing serious work with this you should probably double check. And if you're doing repeated work, obviously get it to write a script or formula instead of doing 1-offs.
It makes sense to use an LLM to write the queries when exploring data, but if you are building a dashboard that’s going to be used repeatedly, you should save the queries so they can be validated and improved over time. The LLM shouldn’t be rewriting a new query every time you open the dashboard.
Definitely. I’ve found Cursor + Grok to be the best budget models for this type of tool use, although I’ve yet to try out the latest Anthropic ones.
You can go “here’s a spreadsheet of some data, do a thing to it” and it’ll go through, have it plan out the type of calculations you want to do, and it’ll write you a program to parse your sheet, calculate what you want, then output an result sheet.
The key to good results with minimal effort seems to be the planning modes to me, combined with the /goal command. A little bit of validation before implementation goes a long way.
They’re very much trained to do this, in fact. Even Step 3.5 Flash, a medium sized open model from way earlier this year is trained to use python to calculate answers on the fly :)
I've seen so many "truthy" results coming back that suggest it's not even remotely as smart as you might hope.
I'm mostly starting to have some success telling TPMs and engineers to ask chatgpt to give them appropriate code that generates more accurate answers.
These days I wouldn't be surprised if the models are smart enough to get the analysis correct.
However sometimes I think that AI use shows a revealed preference around things. In this case, the actual data just ... maybe never mattered to them at all? People wanted a shiny chart and they got a shiny chart.
Either chatgpt or claude are building scripts to do those and has been for a few months at least now.