The problem is they are not i.i.d., so this doesn’t really work. It works a bit, which is in my opinion why chain-of-thought is effective (it gives the LLM a chance to posit a couple answers first). However, we’re already looking at “agents,” so they’re probably already doing chain-of-thought.
I have actually been doing this lately: iteratively prompting AI to write software and fix its errors until something useful comes out. It’s a lot like machine translation. I speak fluent C++, but I don’t speak Rust, but I can hammer away on the AI (with English language prompts) until it produces passable Rust for something I could write for myself in C++ in half the time and effort.
I also don’t speak Finnish, but Google Translate can take what I say in English and put it into at least somewhat comprehensible Finnish without egregious translation errors most of the time.
Is this useful? When C++ is getting banned for “security concerns” and Rust is the required language, it’s at least a little helpful.
I was 0/6 on various trials of AI for Rust over the past 6 months, then I caught a success. Turns out, I was asking it to use a difficult library - I can’t make the thing I want work in that library either (library docs say it’s possible, but…) when I posed a more open ended request without specifying the library to use, it succeeded - after a fashion. It will give you code with cargo build errors, I copy-paste the error back to it like “address: <pasted error message>” and a bit more than half of the time it is able to respond with a working fix.
Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate. LLMs don’t get tired and they can be run in parallel.
What’s 0.7^10?
About 0.02
The problem is they are not i.i.d., so this doesn’t really work. It works a bit, which is in my opinion why chain-of-thought is effective (it gives the LLM a chance to posit a couple answers first). However, we’re already looking at “agents,” so they’re probably already doing chain-of-thought.
Very fair comment. In my experience even increasing the temperature you get stuck in local minimums
I was just trying to illustrate how 70% failure rates can still be useful.
I have actually been doing this lately: iteratively prompting AI to write software and fix its errors until something useful comes out. It’s a lot like machine translation. I speak fluent C++, but I don’t speak Rust, but I can hammer away on the AI (with English language prompts) until it produces passable Rust for something I could write for myself in C++ in half the time and effort.
I also don’t speak Finnish, but Google Translate can take what I say in English and put it into at least somewhat comprehensible Finnish without egregious translation errors most of the time.
Is this useful? When C++ is getting banned for “security concerns” and Rust is the required language, it’s at least a little helpful.
I’m impressed you can make strides with Rust with AI. I am in a similar boat, except I’ve found LLMs are terrible with Rust.
I was 0/6 on various trials of AI for Rust over the past 6 months, then I caught a success. Turns out, I was asking it to use a difficult library - I can’t make the thing I want work in that library either (library docs say it’s possible, but…) when I posed a more open ended request without specifying the library to use, it succeeded - after a fashion. It will give you code with cargo build errors, I copy-paste the error back to it like “address: <pasted error message>” and a bit more than half of the time it is able to respond with a working fix.