Ask Lemmy

Here's an interesting post that gives a pretty good quick summary of when an LLM may be a good tool.

Here's one key:

Machine learning is amazing if:

• The problem is too hard to write a rule-based system for or the requirements change sufficiently quickly that it isn't worth writing such a thing and,
• The value of a correct answer is much higher than the cost of an incorrect answer.

The second of these is really important.

So if your math problem is unsolvable by conventional tools, or sufficiently complex that designing an expression is more effort than the answer is worth... AND ALSO it's more valuable to have an answer than it is to have a correct answer (there is no real cost for being wrong), THEN go ahead and trust it.

If it is important that the answer is correct, or if another tool can be used, then you're better off without the LLM.

The bottom line is that the LLM is not making a calculation. It could end up with the right answer. Different models could end up with the same answer. It's very unclear how much underlying technology is shared between models anyway.

For example, if the problem is something like, "here is all of our sales data and market indicators for the past 5 years. Project how much of each product we should stock in the next quarter. " Sure, an LLM may be appropriately close to a professional analysis.

If the problem is like "given these bridge schematics, what grade steel do we need in the central pylon?" Then, well, you are probably going to be testifying in front of congress one day.