3 ms·

I use LLMs for annoying shit that used to take an inordinate amount of time. For example an analyst gives me a CSV, it has some data clean up issues, eventually

I use LLMs for annoying shit that used to take an inordinate amount of time. For example an analyst gives me a CSV, it has some data clean up issues, eventually it needs to become a series of SQL inserts. What was a day of script writing becomes 15 minutes of prompt engineering to clean up the CSV and convert it into the proper statements. Massive time savings! I believe one can vibe code a basic app or script. Like building one of those little trinkets people put on websites, like calculators or mini games. But I agree, the LLM can’t build, say, Google Chrome, or AWS Cognito, or Netflix, or anything that actually has serious billion dollar value. It’s not a “when” question, because these are not “solvable” problems.

vouaobrasil 1y ago
> I use LLMs for annoying shit that used to take an inordinate amount of time. For example an analyst gives me a CSV, it has some data clean up issues, eventually it needs to become a series of SQL inserts. But that has a flip-side too: analysts are more likely to give you CSVs with more issues because they know you can clean it up with AI. And pretty soon people will put less care in general in what they do until something goes wrong with this translate-with-AI paradigm.
add-sub-mul-div 1y ago
If you learn the skills that's nowhere near a day of work and then you can do it forever without dependence.
nyrikki 1y ago
Honest question, what type of CSV to SQL code are you doing whre correctness doesn't count? Perhaps my few decades in the industry have been in areas whree it is always the details, correctness, and fitness for purpose that tends to make those problems hard, not the work itself. I do see a use case for throw away spikes, or part of a red-green-refactor, etc.. but if accuracy and correctness aren't critical, data cleanup is easy even without an LLM.
- zhivota 1y ago
  In my experience the difficulty in this kind of task is reading the docs of a bunch of packages I haven't used in months/years and probably won't use again anytime soon, testing things manually and creating all the little harnesses to make that work without running for minutes at a time, etc. Sure for someone who does ETL type work all day, or often enough anyway, they'd scoff, and true LLM won't really save them time. But for me who does it once in a blue moon, LLMs are great. It's still on me to determine correctness, I am simply no longer contending with the bootstrap problem of learning new packages and their syntax and common usage.
  - viraptor 1y ago
    Similarly for me, my visualisation pipeline changed from "relearn matplotlib and pandas every single time" to "ask for code, fix up details later". In this case the time saving scales with how much I forgot from the docs and the last time. I need to do the review and debugging either way, so that's moot.
    - esafak 1y ago
      It's not your fault their APIs suck!
  - hooverd 1y ago
    There's two schools of thought here: viewing LLMs as machines to replace your thinking, and viewing LLMs as a vast corpus of compressed knowledge to draw upon.
- nyrikki 1y ago
  > I use LLMs for annoying shit that used to take an inordinate amount of time. For example an analyst gives me a CSV, it has some data clean up issues, eventually it needs to become a series of SQL inserts. The CSV to SQL for analysts problem is a data integrity problem that is domain specific and not tool specific. Remember that a 'relation' in relational databases is just a table, specifically named columns and tuples (rows). A CSV is also just tuples (lines), but obviously SQL also typically has multiple normalized tables etc... Typically bad data is worse than missing data. For analysts, missing data can lead to bias and reduced statistical challenges, but methods exist and it can often be handled. Bad data, on the other hand, can be misleading, deceptive and/or harmful. An LLM will be its very nature, be likely to produce bad data when cleaning. The risk of using an LLM here is that it doesn't have context or nuances to deal with that. Data cleaning via (sed,grep,tr,awk), language tools or even ETL can work.... I promise you that fixing that bad data will be far worse. But using it in a red-green-refactor model may help with the above, but you will actively need to be engaged and dig through what it produces. Personally I find it takes more time to do that than to just view it as tuple repacking...and use my favorite tools to do so. Data cleaning is hard, but it is the context specific details that make it so.
rhubarbtree 1y ago
How do you check to make sure that the LLM hasn’t deleted any data, or changed something you didn’t want to be changed? A colleague did this recently and found it had removed crucial data. Better to get it to help you author scripts or difficult parts of scripts, and then review the code carefully.
nottorp 1y ago
The problem with statements like yours is that everyone praises the LLM for doing things they (the humans praising) can do in their sleep. You sound like you've done that CSV to SQL a lot of times, are subconsciously aware of all the pitfalls and unwritten (and perhaps never specified) requirements and you're limited by just your typing speed when doing it again. I can use LLMs for stuff I can do in my sleep as well. I move that even for you or me, LLMs ain't worth much for stuff we can't do in our sleep. Stuff we don't know or have only introductory knowledge of. You can use them to generate tutorials instead of searching for them, but that's about it. Generating tutorials is good, but is it good because a LLM did it or because you can't find a good tutorial by searching any more?

vouaobrasil 1y ago

> I use LLMs for annoying shit that used to take an inordinate amount of time. For example an analyst gives me a CSV, it has some data clean up issues, eventually it needs to become a series of SQL inserts. But that has a flip-side too: analysts are more likely to give you CSVs with more issues because they know you can clean it up with AI. And pretty soon people will put less care in general in what they do until something goes wrong with this translate-with-AI paradigm.

add-sub-mul-div 1y ago

If you learn the skills that's nowhere near a day of work and then you can do it forever without dependence.

nyrikki 1y ago

Honest question, what type of CSV to SQL code are you doing whre correctness doesn't count? Perhaps my few decades in the industry have been in areas whree it is always the details, correctness, and fitness for purpose that tends to make those problems hard, not the work itself. I do see a use case for throw away spikes, or part of a red-green-refactor, etc.. but if accuracy and correctness aren't critical, data cleanup is easy even without an LLM.

zhivota 1y ago
In my experience the difficulty in this kind of task is reading the docs of a bunch of packages I haven't used in months/years and probably won't use again anytime soon, testing things manually and creating all the little harnesses to make that work without running for minutes at a time, etc. Sure for someone who does ETL type work all day, or often enough anyway, they'd scoff, and true LLM won't really save them time. But for me who does it once in a blue moon, LLMs are great. It's still on me to determine correctness, I am simply no longer contending with the bootstrap problem of learning new packages and their syntax and common usage.
- viraptor 1y ago
  Similarly for me, my visualisation pipeline changed from "relearn matplotlib and pandas every single time" to "ask for code, fix up details later". In this case the time saving scales with how much I forgot from the docs and the last time. I need to do the review and debugging either way, so that's moot.
  - esafak 1y ago
    It's not your fault their APIs suck!
- hooverd 1y ago
  There's two schools of thought here: viewing LLMs as machines to replace your thinking, and viewing LLMs as a vast corpus of compressed knowledge to draw upon.
nyrikki 1y ago
> I use LLMs for annoying shit that used to take an inordinate amount of time. For example an analyst gives me a CSV, it has some data clean up issues, eventually it needs to become a series of SQL inserts. The CSV to SQL for analysts problem is a data integrity problem that is domain specific and not tool specific. Remember that a 'relation' in relational databases is just a table, specifically named columns and tuples (rows). A CSV is also just tuples (lines), but obviously SQL also typically has multiple normalized tables etc... Typically bad data is worse than missing data. For analysts, missing data can lead to bias and reduced statistical challenges, but methods exist and it can often be handled. Bad data, on the other hand, can be misleading, deceptive and/or harmful. An LLM will be its very nature, be likely to produce bad data when cleaning. The risk of using an LLM here is that it doesn't have context or nuances to deal with that. Data cleaning via (sed,grep,tr,awk), language tools or even ETL can work.... I promise you that fixing that bad data will be far worse. But using it in a red-green-refactor model may help with the above, but you will actively need to be engaged and dig through what it produces. Personally I find it takes more time to do that than to just view it as tuple repacking...and use my favorite tools to do so. Data cleaning is hard, but it is the context specific details that make it so.

rhubarbtree 1y ago

How do you check to make sure that the LLM hasn’t deleted any data, or changed something you didn’t want to be changed? A colleague did this recently and found it had removed crucial data. Better to get it to help you author scripts or difficult parts of scripts, and then review the code carefully.

nottorp 1y ago

The problem with statements like yours is that everyone praises the LLM for doing things they (the humans praising) can do in their sleep. You sound like you've done that CSV to SQL a lot of times, are subconsciously aware of all the pitfalls and unwritten (and perhaps never specified) requirements and you're limited by just your typing speed when doing it again. I can use LLMs for stuff I can do in my sleep as well. I move that even for you or me, LLMs ain't worth much for stuff we can't do in our sleep. Stuff we don't know or have only introductory knowledge of. You can use them to generate tutorials instead of searching for them, but that's about it. Generating tutorials is good, but is it good because a LLM did it or because you can't find a good tutorial by searching any more?