anon 0x51e said in #2971 13h ago:
I would like to start a thread to share the methods we employ to use LLMs in a way that enhances our abilities, rather than just lazily outsources tasks to them. The heuristic for the techniques I am looking for would be if after employing the technique, are you more capable or knowledgable than you were before. In other words, even if you never had access to an LLM again, would you still be better off.
To start off here are some strategies and the contexts in which I have employed them:
In studying Ancient Greek, I take a given sentence and ask the LLM to *not* translate it, but instead I will give me best shot at a translation, and it should you the socratic method to guide me to better understanding of the text through questions. This typically reminds me of grammar I have forgotten, gives me more nuanced understanding of vocabulary, or just helps me identify parsing mistakes.
Learning hot to use a specific libraries that are publicly available through more refined examples and targeted questions. When trying to use an library or API, you typically just want some answers to common questions so you'll know how to make design decisions. LLMs are superior to reading docs, as you can ask more targeted questions to build your initial understanding and help you know how to go through the docs.
Getting feedback on written works with nuanced context. I was writing a poem for someone, and I was able to do the work myself to write the first few passes, then give it to an LLM and ask for critiques. It was able to point out the weakest aspects of the poem so I could go back and target those. Similarly, I was in negotiations and discussing the situation helped me rephrase a proposal in a way that made it easier to fulfill. Previously, I wouldn't have known to phrase it this way as it wasn't a common situation for me.
Web searches in general to answer idle curiosities. From mathematical topics I vaguely understand where I can now get a primer custom tailored to my knowledge, to complex theological dogma questions and exploring the different viewpoints. Previously, I would either just not look for this information because it would involve more reading and study than I would care to do. Now the bar is lower for learning small interesting tidbits. This may be the weakest of the techniques as I am unsure whether this is long term beneficial, rather than just not knowing things. I like to believe wide knowledge will be useful though.
When coding, LLMs are good to help make a plan of attack and consider tradeoffs. Basically just as a rubber duck that know about all the standard libraries and system calls and faintly recalls every blog post ever written. Previously, I would maybe write in a text file what my plan was before I started executing, but typically I would just start and thus my efforts would be less intentional. The LLMs just make this all a bit more intentional and help me realize problems before they happen.
In techniques that don't work, I have yet to find their code good for any existing codebase, but they can spit out a proof of concept pretty easily with *heavy* guidance and guardrails about technical decisions and priorities. I don't count this use case for this thread, as it doesn't pass our heuristic of whether it will be useful long term even without access to an LLM.
Please share your experiments and techniques, both successful and failed.
To start off here are some strategies and the contexts in which I have employed them:
In studying Ancient Greek, I take a given sentence and ask the LLM to *not* translate it, but instead I will give me best shot at a translation, and it should you the socratic method to guide me to better understanding of the text through questions. This typically reminds me of grammar I have forgotten, gives me more nuanced understanding of vocabulary, or just helps me identify parsing mistakes.
Learning hot to use a specific libraries that are publicly available through more refined examples and targeted questions. When trying to use an library or API, you typically just want some answers to common questions so you'll know how to make design decisions. LLMs are superior to reading docs, as you can ask more targeted questions to build your initial understanding and help you know how to go through the docs.
Getting feedback on written works with nuanced context. I was writing a poem for someone, and I was able to do the work myself to write the first few passes, then give it to an LLM and ask for critiques. It was able to point out the weakest aspects of the poem so I could go back and target those. Similarly, I was in negotiations and discussing the situation helped me rephrase a proposal in a way that made it easier to fulfill. Previously, I wouldn't have known to phrase it this way as it wasn't a common situation for me.
Web searches in general to answer idle curiosities. From mathematical topics I vaguely understand where I can now get a primer custom tailored to my knowledge, to complex theological dogma questions and exploring the different viewpoints. Previously, I would either just not look for this information because it would involve more reading and study than I would care to do. Now the bar is lower for learning small interesting tidbits. This may be the weakest of the techniques as I am unsure whether this is long term beneficial, rather than just not knowing things. I like to believe wide knowledge will be useful though.
When coding, LLMs are good to help make a plan of attack and consider tradeoffs. Basically just as a rubber duck that know about all the standard libraries and system calls and faintly recalls every blog post ever written. Previously, I would maybe write in a text file what my plan was before I started executing, but typically I would just start and thus my efforts would be less intentional. The LLMs just make this all a bit more intentional and help me realize problems before they happen.
In techniques that don't work, I have yet to find their code good for any existing codebase, but they can spit out a proof of concept pretty easily with *heavy* guidance and guardrails about technical decisions and priorities. I don't count this use case for this thread, as it doesn't pass our heuristic of whether it will be useful long term even without access to an LLM.
Please share your experiments and techniques, both successful and failed.
I would like to star