Something’s been bugging me about how new devs and I need to talk about it. We’re at this weird inflection point in software development. Every junior dev I talk to has Copilot or Claude or GPT running 24/7. They’re shipping code faster than ever. But when I dig deeper into their understanding of what they’re shipping? That’s where things get concerning. Sure, the code works, but ask why it works that way instead of another way? Crickets. Ask about edge cases? Blank stares. The foundational knowledge that used to come from struggling through problems is just… missing. We’re trading deep understanding for quick fixes, and while it feels great in the moment, we’re going to pay for this later.
I remember talking to someone about where LLMs are and aren’t useful. I pointed out that LLMs would be absolutely worthless for me as my work mostly consists of interacting with company-internal APIs, which the LLM obviously hasn’t been trained on.
The other person insisted that that is exactly what LLMs are great at. They wouldn’t explain how exactly the LLM was supposed to know how my company’s internal software, which is a trade secret, is structured.
But hey, I figured I’d give it a go. So I fired up a local Llama 3.1 instance and asked it how to set up a local copy of ASDIS, one such internal system (name and details changed to protect the innocent). And Llama did give me instructions… on how to write the American States Data Information System, a Python frontend for a single MySQL table containing basic information about the member states of the USA.
Oddly enough, that’s not what my company’s ASDIS is. It’s almost as if the LLM had no idea what I was talking about. Words fail to express my surprise at this turn of events.
Yeah, and the way it will confidently give you a wrong answer instead of either asking for more information or saying it just doesn’t know is equally annoying.
Because giving answers is not a LLM’s job. A LLM’s job is to generate text that looks like an answer. And we then try to coax framework that into generating correct answers as often as possible, with mixed results.
I remember talking to someone about where LLMs are and aren’t useful. I pointed out that LLMs would be absolutely worthless for me as my work mostly consists of interacting with company-internal APIs, which the LLM obviously hasn’t been trained on.
The other person insisted that that is exactly what LLMs are great at. They wouldn’t explain how exactly the LLM was supposed to know how my company’s internal software, which is a trade secret, is structured.
But hey, I figured I’d give it a go. So I fired up a local Llama 3.1 instance and asked it how to set up a local copy of ASDIS, one such internal system (name and details changed to protect the innocent). And Llama did give me instructions… on how to write the American States Data Information System, a Python frontend for a single MySQL table containing basic information about the member states of the USA.
Oddly enough, that’s not what my company’s ASDIS is. It’s almost as if the LLM had no idea what I was talking about. Words fail to express my surprise at this turn of events.
Yeah, and the way it will confidently give you a wrong answer instead of either asking for more information or saying it just doesn’t know is equally annoying.
Because giving answers is not a LLM’s job. A LLM’s job is to generate text that looks like an answer. And we then try to coax framework that into generating correct answers as often as possible, with mixed results.