this post was submitted on 12 Apr 2024
73 points (95.1% liked)
Technology
59605 readers
3302 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
One way it can be useful is when you use it as a more verbal variant of rubber duck debugging. You'll need to state the issue that you're facing, including the context and edge cases. In doing so, the problem will also become more clear to you yourself.
Contrary to a rubber duck, it can then actually suggest some approach vectors, which you can then dismiss or investigate further.
This is how I use LLMs right now, and there have been a few times it's been genuinely helpful. Mind you, most of the time it's been helpful, it's because it hallucinates some nonsense that gets me in the right direction, but that's still at least a little better than the duck.
That was my experience as well with GPT 3.5. But the hit ratio is a lot better with GPT 4, and other models like Mixtral and its derivatives.