This content originally appeared on Adactio: Journal and was authored by Adactio: Journal
I don’t use large language models. My objection is to using them is ethical. I know how the sausage is made.
I wanted to clarify that. I’m not rejecting large language models because they’re useless. They can absolutely be useful. I just don’t think the usefulness outweighs the ethical issues in how they’re trained.
Molly White came to the same conclusion:
The benefits, though extant, seem to pale in comparison to the costs.
What I do know is that I find LLMs useful on occasion, but every time I use one I die a little inside.
I genuinely look forward to being able to use a large language model with a clear conscience. Such a model would need to be trained ethically. When we get a free-range organic large language model I’ll be the first in line to use it. Until then, I’ll abstain. Remember:
You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.
Still, in anticipation of an ethical large language model someday becoming reality, I think it’s good for me to have an understanding of which tasks these tools are good at.
Prototyping seems like a good use case. My general attitude to prototyping is the exact opposite to my attituted to production code; use absolutely any tool you want and prioritise speed over quality.
When it comes to coding in general, I think Laurie is really onto something when he says:
Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.
In other words, despite what the hype says, these tools are far better at transforming than they are at generating.
Iris Meredith goes deeper into this distinction between transformative and compositional work:
Compositionality relies (among other things) on two core values or functions: choice and precision, both of which are antithetical to LLM functioning.
My own take on this is that transformative work is often the drudge work—take this data dump and convert it to some other format; take this mock-up and make a disposable prototype. I want my tools to help me with that.
But compositional work that relies on judgement, taste, and choice? Not only would I not use a large language model for that, it’s exactly the kind of work that I don’t want to automate away.
Transformative work is done with broad brushstrokes. Compositional work is done with a scalpel.
Large language models are big messy brushes, not scalpels.
This content originally appeared on Adactio: Journal and was authored by Adactio: Journal
Adactio: Journal | Sciencx (2025-05-27T16:00:29+00:00) Uses. Retrieved from https://www.scien.cx/2025/05/27/uses/
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