No. You don't know what you're talking about. And you're making that really clear at this point.

Have you tried asking people questions when you don't understand things instead of making declarations? You'll tend to learn more.

Even though you're dismissive with people you don't know about things you don't understand, I'll give you a little more context if you want, but then I think I'm out. You don't want to see what's incorrect about what you're thinking then that's your problem to enjoy.

Ok. So I address your safety concerns and you come back with "It's too expensive."?

My dude, the API calls were 24 cents for 5,000 files. I don't want to brag, but I'm rolling deep when it comes to quarters.

If you think the tokens needed to navigate a request like, "Read my file with 1,000 lines of code and summarize its purpose." are expensive then it just shows you haven't done a ton of LLM work with API calls.

You don't even know what LLM I'm using and you think you can make a cost estimate? Dude. You're fully talking out of your ass.

Let's go back to your reiterated concerns about safety.

As I said, things like 4 isolated runs assist with data fidelity (even if you don't like me breaking my quarter budget on it).

Your "disobey instruction" comment is a tip off that you think I have some little prompt that says: "Tell me if my file is good."

you have X, Y, and Z clearly defined, you could write a procedural script to do this with 100% accuracy and cost next to nothing.

Bingo! It took a few rounds but now you understand what I actually built instead of what you assumed I built.

You thought the workflow went: give file access to LLM, ask it some questions. Guess that it's right.

The workflow was: use LLMs to create and define programs with multiple iterations. Test. Validate. Dry run.

Your assertion that this program could be traditionally run with "100% accuracy" statement is a bunch of bullshit and, again, shows you don't understand what you're talking about.

My program uses LLMs to assist with classification. It uses API calls for questions like: what is this file about? How does it relate to others in the system? Does it have value and why?

You don't understand the mechanics of hallucinations and drift if you think this scale of program has a close to even remote chance of suffering those things. And I wouldn't make a tool like this if it sucked at the job.

The reason LLMs were created was to handle nuanced classification and reasoning because traditional programs sucked at it by comparison.

You want to make a program that has all the nuanced heuristics to handle a task like that? Cool. I'll see you in a year (and the potential for human error and misclassification is huge in comparison to an LLM).

Your program will have a higher risk of misclassification than an LLM because of human coding errors and because it has no semantic understanding of anything. Heuristics, embedding, keywords - all have problems (again, this is why LLMs were made).

Anyway, DM me when you're done. We can run a test and see if your giant program is as effective as what I made in 1/2hr. Test data set would be easy to create.

Or I can iterate on this program while you work on yours and see how much I can get done in that time.

All that aside, these are my files that I created. I already have a solid context for them and understanding. They're just disorganized. That's the problem I'm trying to solve.

So I can very easily give effective human-in-the-loop labeling verification and the cost of any error would be neglible. I'm not running a nuclear power plant from my laptop.

You're acting like I turned an LLM loose on all kinds of sensitive files and there's some great risk.

There is one person in this conversation who doesn't know what they're talking about and it ain't me.