LLMs are great at checking grammar in writing. That’s the other thing I’ve found there useful for 🤷
Basically, using LLMs to write something is always a bad idea (unless you’re responding to bullshit with more bullshit e.g. work emails 🤣). Using them to check writing is pretty useful though.
AbelianGrape@beehaw.org 3 weeks ago
I’ve only tried a handful of times, but I’ve never been able to get an LLM to do a grunt refactoring task that didn’t require me to rewrite all the output again anyway.
DaRizat@piefed.social 3 weeks ago
You have to invest in setting it up for success. Give it a really good context, feed it docs or other resources through MCP servers, use a memory bank pattern.
I just did a 30k contract with it where I hand wrote probably 20% of the code, and 75% of that was probably me just reviewing the diffs the LLM made like a PR. But that doesn't mean I'm vibe coding, I feed it atomic operations and review each change as if it was a PR. I come away understanding the totality of the code so that I can debug easily when things go wrong.
You can't just go "Here's my idea; Make it." That probably will never happen (even though that's the kool-aid that's being served), but if you're disciplined and make the most of the tools available it can absolutely 3-5x your output as an engineer.
AbelianGrape@beehaw.org 3 weeks ago
The LLM in the most recent case had a monumental amount of context. I then gave it a file implementing a breed of hash set, asked it to explain several of the functions which it did correctly, and then asked it to convert it to a hash map implementation (an entirely trivial, grunt change).
It spat out the source code of the tree-based map implementation in the standard library.
orca@orcas.enjoying.yachts 3 weeks ago
The trick is giving it tons of context. It also depends on the LLM. Claude has given me the most success.