His point is that an LLM is just another tool for increasing productivity, the same as a compiler or IDE. The quality of the code is the only metric that should be judged.
Except they’re not? Very few tools are deterministic. Only Mathematics is, and that’s only a small subset of tools.
You’re free to argue that non-deterministic tools lack value, but that’s clearly false: OCR only exists because of lossy training machine learning algorithms. Or, going even more basic, Monte Carlo simulations are by far the most efficient method of testing entire categories of problems that are intractable deterministically.
LLMs can be shitty, as a business practice, due to their unpredictability, but (hopefully obviously) that depends on how it’s used.
The point is a tool is something you wield. Your skill is needed for and improved by the use of a tool. An LLM is an automation. You don’t wield it, you give directives. Your skill is unnecessary for its use and is left untrained. The worst part is that, unlike a good, deterministic automation (like cruise control), where you understand the limitations and can take over to avoid them, you can’t do that with LLMs. The best you can do is carefully review the slop output.
I’d argue that Monte Carlo is a bad example here, and is an example of a deterministic tool.
You use a tool to create an outcome, you’re looking for an answer or a solution that you can trust.
If you want to examine behavior of a system that has input variables across some known range, you could brute force it, try every single possible combination and wait a millennia. Or you use a monte carlo strategy, let randomness enter the simulation to build out a basic characterization. You want more detail? More confidence? Increase the number of runs. I can trust that if I increase the number of runs I gain higher fidelity, if the tool/strategy was non deterministic I couldn’t do that
Assuming you didn’t know what tools were used to write the code that you are reviewing, how would reviewing it be any different? You would still ask the human author questions about intent if that were not clear. The quality of their responses may or may not be impacted by their use of LLMs, but that is up for you to reason about as the reviewer, and it’s perfectly fine to tell someone they are being a jackass for only sending slop to you.
His point is that an LLM is just another tool for increasing productivity, the same as a compiler or IDE. The quality of the code is the only metric that should be judged.
And the person you replied to is saying it is not just another tool because tools are deterministic.
Except they’re not? Very few tools are deterministic. Only Mathematics is, and that’s only a small subset of tools.
You’re free to argue that non-deterministic tools lack value, but that’s clearly false: OCR only exists because of lossy training machine learning algorithms. Or, going even more basic, Monte Carlo simulations are by far the most efficient method of testing entire categories of problems that are intractable deterministically.
LLMs can be shitty, as a business practice, due to their unpredictability, but (hopefully obviously) that depends on how it’s used.
tell us of a software tool that isn’t deterministic.
The point is a tool is something you wield. Your skill is needed for and improved by the use of a tool. An LLM is an automation. You don’t wield it, you give directives. Your skill is unnecessary for its use and is left untrained. The worst part is that, unlike a good, deterministic automation (like cruise control), where you understand the limitations and can take over to avoid them, you can’t do that with LLMs. The best you can do is carefully review the slop output.
I’d argue that Monte Carlo is a bad example here, and is an example of a deterministic tool.
You use a tool to create an outcome, you’re looking for an answer or a solution that you can trust.
If you want to examine behavior of a system that has input variables across some known range, you could brute force it, try every single possible combination and wait a millennia. Or you use a monte carlo strategy, let randomness enter the simulation to build out a basic characterization. You want more detail? More confidence? Increase the number of runs. I can trust that if I increase the number of runs I gain higher fidelity, if the tool/strategy was non deterministic I couldn’t do that
Reviewing AI code is harder than reviewing human code. I’ll judge humans on what they do, and LLMs on what they are.
Assuming you didn’t know what tools were used to write the code that you are reviewing, how would reviewing it be any different? You would still ask the human author questions about intent if that were not clear. The quality of their responses may or may not be impacted by their use of LLMs, but that is up for you to reason about as the reviewer, and it’s perfectly fine to tell someone they are being a jackass for only sending slop to you.