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Can LLMs write better code if you keep asking them to “write better code”?

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Re: Can LLMs write better code if you keep asking them to “write better code”?

#141

Post by cratermoon »

scosman wrote: post #118

By iterating it 5 times the author is using ~5x the compute. It’s kinda a strange chain of thought. Also: premature optimization is evil. I like the first iteration most. It’s not “beginner code”, it’s simple. Tell sonnet to optimize it IF benchmarks show it’s a pref problem. But a codebase full of code like this, even when unnecessary, would be a nightmare.

So in this article "better" means "faster". This demonstrates that "better" is an ambiguous measure and LLMs will definitely trip up on that.

Also, the article starts out talking about images and the "make it more X" prompt and says how the results are all "very samey and uninteresting" and converge on the same vague cosmic-y visuals. What does the author expect will happen to code given the "make it more X" treatment?

Re: Can LLMs write better code if you keep asking them to “write better code”?

#142

Post by btbuildem »

dash2 wrote: post #5

Something major missing from the LLM toolkit at the moment is that it can't actually run (and e.g. test or benchmark) its own code. Without that, the LLM is flying blind. I guess there are big security risks involved in making this happen. I wonder if anyone has figured out what kind of sandbox could safely be handed to a LLM.

I think the GPT models have been able to run Python (albeit limited) for quite a while now. Expanding that to support a variety of programming languages that exist though? That seems like a monumental task with relatively little reward.

Re: Can LLMs write better code if you keep asking them to “write better code”?

#143

Post by stormfather »

I made an objective test for prompting hacks last year.

I asked gpt-4-1106-preview to draw a bounding box around some text in an image and prodded in various ways to see what moved the box closer. Offering a tip did in fact help lol so that went into the company system prompt.

IIRC so did most things, including telling it that it was on a forum, and OP had posted an incorrect response, which gpt was itching to correct with its answer.

Re: Can LLMs write better code if you keep asking them to “write better code”?

#144

Post by HarHarVeryFunny »

This seems like anthromorphizing the model ... Occam's Razor says that the improvement coming from iterative requests to improve the code comes from the incremental iteration, not incentivizing the model to do it's best. If the latter were the case then one could get the best version on first attempt by telling it your grandmother's life was on the line or whatever.

Reasoning is known weakness of these models, so jumping from requirements to a fully optimized implementation that groks the solution space is maybe too much to expect - iterative improvement is much easier.

Re: Can LLMs write better code if you keep asking them to “write better code”?

#145

Post by nahnahno »

scosman wrote: post #118

By iterating it 5 times the author is using ~5x the compute. It’s kinda a strange chain of thought. Also: premature optimization is evil. I like the first iteration most. It’s not “beginner code”, it’s simple. Tell sonnet to optimize it IF benchmarks show it’s a pref problem. But a codebase full of code like this, even when unnecessary, would be a nightmare.

This is not what premature optimization is the root of all evil means. It’s a tautological indictment of doing unnecessary things. It’s not in support of making obviously naive algorithms. And if it were it wouldn’t be a statement worth focusing on.

As the point of the article is to see if Claude can write better code from further prompting so it is completely appropriate to “optimize” a single implementation.

Re: Can LLMs write better code if you keep asking them to “write better code”?

#146

Post by cratermoon »

juujian wrote: post #55

I often run into LLMs writing "beginner code" that uses the most fundamental findings in really impractical ways. Trained on too many tutorials I assume. Usually, specifying the packages to use and asking for something less convoluted works really well. Problem is, how would you know if you have never learned to code without an LLM?

LLMs are trained on content from places like Stack Overflow, reddit, and github code, and they generate tokens calculated as a sort of aggregate statistically likely mediocre code. Of course the result is going be uninspired and impractical. Writing good code takes more than copy-pasting the same thing everyone else is doing.

Re: Can LLMs write better code if you keep asking them to “write better code”?

#147

Post by simonw »

dash2 wrote: post #5

Something major missing from the LLM toolkit at the moment is that it can't actually run (and e.g. test or benchmark) its own code. Without that, the LLM is flying blind. I guess there are big security risks involved in making this happen. I wonder if anyone has figured out what kind of sandbox could safely be handed to a LLM.

I've been closely following this area - LLMs with the ability to execute code in a sandbox - for a while.

ChatGPT was the first to introduce this capability with Code Interpeter mode back in around March 2023: https://simonwillison.net/tags/code-interpreter/

This lets ChatGPT write and then execute Python code in a Kubernetes sandbox. It can run other languages too, but that's not documented or supported. I've even had it compile and execute C before: https://simonwillison.net/2024/Mar/23/building-c-extensions-...

Gemini can run Python (including via the Gemini LLM API if you turn on that feature) but it's a lot more restricted than ChatGPT - I don't believe it can install extra wheels, for example.

Claude added the ability to write and execute JavaScript recently (October), which happens in a sandbox in the user's browser, not on their servers: https://simonwillison.net/2024/Oct/24/claude-analysis-tool/

Claude also has Artifacts, which can write a UI in HTML and JavaScript and show that to the user... but can't actually execute code in a way that's visible to the LLM itself so doesn't serve the same feedback look purposes as those other tools. https://simonwillison.net/tags/claude-artifacts/

In December ChatGPT added Canvas which can execute Python in the user's browser, super confusing because they already have a separate Python system in Code Interpreter: https://simonwillison.net/2024/Dec/10/chatgpt-canvas/#canvas...

Re: Can LLMs write better code if you keep asking them to “write better code”?

#148

Post by bilbo0s »

btbuildem wrote: post #140

I've noticed this with GPT as well -- the first result I get is usually mediocre and incomplete, often incorrect if I'm working on something a little more obscure (eg, OpenSCAD code). I've taken to asking it to "skip the mediocre nonsense and return the good solution on the first try". The next part is a little strange - it arose out of frustration, but it also seems to improve results. Let's call it "negative incent…

"If you get this wrong, OpenAI will be sued for a lot of money, and all the board members will go to prison"

This didn't work. At least not on my task. What model were you using?

Re: Can LLMs write better code if you keep asking them to “write better code”?

#149

Post by RobotToaster »

btbuildem wrote: post #140

I've noticed this with GPT as well -- the first result I get is usually mediocre and incomplete, often incorrect if I'm working on something a little more obscure (eg, OpenSCAD code). I've taken to asking it to "skip the mediocre nonsense and return the good solution on the first try". The next part is a little strange - it arose out of frustration, but it also seems to improve results. Let's call it "negative incent…

IIRC there was a post on here a while ago about how LLMs give better results if you threaten them or tell them someone is threatening you (that you'll lose your job or die if it's wrong for instance)

Re: Can LLMs write better code if you keep asking them to “write better code”?

#150

Post by gadders »

dgacmu wrote: post #80

I'm amused that neither the LLM or the author identified one of the simplest and most effective optimizations for this code: Test if the number is max _before_ doing the digit sum. It's a free 5.5x speedup that renders some of the other optimizations, like trying to memoize digit sums, unnecessary. On an m1 macbook pro, using numpy to generate the random numbers, using mod/div to do digit sum: Base: 55ms Test before…

I had a scan of the code examples, but one other idea that occurred to me is that you could immediately drop any numbers below 999 (probably slightly higher, but that would need calculation rather than being intuitive).
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