Comment by sillyfluke
5 hours ago
>I'd still place a bet that the SOA models make _far_ less mistakes than humans.
Genuine question: your top coder seems to be producing the most error-free code from your perspective, has the deepest knowledge of the architecture and codebase, and is faster on the trigger than the others.
But your top coder has proven and verifiable dementia, where they will confidently assume the existence of apis and code that do not exist, mix up the purpose of others and forget other things, and you can't predict when and how they will introduce errors into the system or the severity of such errors.
Are you really comfortable letting this person with dementia generate most of your codebase in the airline and health industry?
I also hope you have an iron-clad agreement that prevents the model provider from doing silent updates because all your evidence of correctness you collected thus far goes out the window in that case.
Another genuine question:
You have witnessed a human coder and the AI you're using make the same important mistake. Assuming you do not have the time and resources to retrain, fine tume, and test your frontier model:
Who would you trust not to make the same mistake multiple times in the future after you have warned them that their job depends on it, the AI or the human?
Your top coder has guard rails in place to prevent him autonomously going free - right? This is how you should approach agentic development with LLMs. Like it or not, we are the final bastion, the gatekeepers. The hallucination thing I think is mostly overblown and from speaking to colleagues it seems to vary wildly depending on which model and harness you are using - always go for SOA. In the last 3 months I can count on one hand where it's done something wrong and that's primarily as I'm operating it with guard rails and giving it context.
>Your top coder has guard rails in place to prevent him autonomously going free - right?
The parent is implying they would prefer an AI when working in the airline and health industry because it makes less errors. Read the comment again.
They have not said, "Hey, I work in the airline and health industry and I'd love to use AI for a couple of the bullshit IT UIs we have as long as we can put guardrails on the AI to stay in its lane."
I asked a yes or no question. The guardrails you can put to mitigate errors are the same guardrails pre-AI for the humans (tests, regressions, reviews). If you were wary of employing a top lead engineer with verifiable dementia prior to AI for a mission critical system, logic implies you should think twice giving that much responsibility to an AI as well.
> The hallucination thing I think is mostly overblown
Can you predict when and how the SOTA model will hallucinate? Yes or no. Can you predict the severity impact of that error beforehand? Yes or no.
>from speaking to colleagues it seems to vary wildly depending on which model and harness you are using
You have partially answered my question it would seem.
> Can you predict when and how the SOTA model will hallucinate? Yes or no. Can you predict the severity impact of that error beforehand? Yes or no.
No, but the same can be said for your colleagues. You might call what the LLM does hallucinations, I'd call them mistakes. I think we have totally forgotten that humans make them all the time and are confidently wrong too.
Your original question, doesn't really get to the bottom of the point I'm trying to make, and I don't really feel it fairly represents the issue we are talking about here. They are not the same things.
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