Researchers have found that large language models (LLMs) tend to parrot buggy code when tasked with completing flawed snippets.
That is to say, when shown a snippet of shoddy code and asked to fill in the blanks, AI models are just as likely to repeat the mistake as to fix it.
Mmm it sounds like you’re using it in a very different way to me; by the time I’m using an LLM, I generally have way more than a general feel for what I’m looking for. People rag on ai for being a “fancy autocomplete”, but that’s literally what I like to use it for. I’ll feed it a detailed spec for what I need, give it a skeleton function with type definitions, and tell the ai to fill it in. It generally fills in basic functions pretty well with that level of definition (ymmv depending on the scope of the function).
This lets me focus more on the code design/structure and validation, while the ai handles a decent amount of grunt work. And if it does a bad job, I would have written the spec and skeleton anyways, so it’s more like bonus if it works. It’s also very good at imitation, so it can help to avoid double-work with similar functionalities.
Kind of shortened/naive example of how I use:
/* Example of another db update function within the app */ /* UnifiedEventUpdate and UnifiedEvent type definitions */Help me fill in this function
/// Updates event properties, and children: /// - If `event.updated` is newer than existing, update as normal /// - If `event.updated` is older than existing, error /// - If no `event.updated` is provided, assume updated to be now() /// For updating Content(s): /// - If `content.id` exists, update the existing content /// - If `content.id` does not exist, create a new content /// - If an existing content isn't present, delete the content pub fn update_event( conn: &mut Conn, event: UnifiedEventUpdate, ) -> Result<UnifiedEvent, Error> {