ok so, i have large reservations with how LLM’s are used. but when used correctly they can be helpful. but where and how?

if you were to use it as a tutor, the same way you would ask a friend what a segment of code does, it will break down the code and tell you. and it will get as nity grity, and elementary school level as you weir wish without judgement, and i in what ever manner you prefer, it will recommend best practices, and will tell you why your code may not work with the understanding that it does not have the knowledge of the project you are working on. (it’s not going to know the name of the function you are trying to load, but it will recommend checking for that in trouble shooting).

it can rtfm and give you the parts you need for any thing with available documentation, and it will link to it so you can verify it, wich you should do often, just like you were taught to do with wikipedia articles.

if you ask i it for code, prepare to go through each line like a worksheet from high school to point out all the problems, wile good exercise for a practicle case, being the task you are on, it would be far better to write it yourself because you should know the particulars and scope.

also it will format your code and provide informational comments if you can’t be bothered, though it will be generic.

again, treat it correctly for its scope, not what it’s sold as by charletons.

I'm not the person you're replying to but the one thing I've found them helpful for is targeted search.

I can ask it a question and then access its sources from whatever response it generates to read and review myself.

Kind of a simpler, free LexisNexis.

Vibe coding you do end up spending a lot of time waiting for prompts, so I get the results of that study.

I fall pretty deep in the power user category for LLMs, so I don’t really feel that the study applies well to me, but also I acknowledge I can be biased there.

I have custom proprietary MCPs for semantic search over my code bases that lets AI do repeated graph searches on my code (imagine combining language server, ctags, networkx, and grep+fuzzy search). That is way faster than iteratively grepping and code scanning manually with a low chance of LLM errors. By the time I open GitHub code search or run ripgrep Claude has used already prioritized and listed my modules to investigate.

That tool alone with an LLM can save me half a day of research and debugging on complex tickets, which pays for an AI subscription alone. I have other internal tools to accelerate work too.

I use it to organize my JIRA tickets and plan my daily goals. I actually get Claude to do a lot of triage for me before I even start a task, which cuts the investigation phase to a few minutes on small tasks.

I use it to review all my PRs before I ask a human to look, it catches a lot of small things and can correct them, then the PR avoids the bike shedding nitpicks some reviewers love. Claude can do this, Copilot will only ever point out nitpicks, so the model makes a huge difference here. But regardless, 1 fewer review request cycle helps keep things moving.

It’s a huge boon to debugging — much faster than searching errors manually. Especially helpful on the types of errors you have to rabbit hole GitHub issue content chains to solve.

It’s very fast to get projects to MVP while following common structure/idioms, and can help write unit tests quickly for me. After the MVP stage it sucks and I go back to manually coding.

I use it to generate code snippets where documentation sucks. If you look at the ibis library in Python for example the docs are Byzantine and poorly organized. LLMs are better at finding the relevant docs than I am there. I mostly use LLM search instead of manual for doc search now.

I have a lot of custom scripts and calculators and apps that I made with it which keep me more focused on my actual work and accelerate things.

I regularly have the LLM help me write bash or python or jq scripts when I need to audit codebases for large refactors. That’s low maintenance one off work that can be easily verified but complex to write. I never remember the syntax for bash and jq even after using them for years.

I guess the short version is I tend to build tools for the AI, then let the LLM use those tools to improve and accelerate my workflows. That returns a lot of time back to me.

I do try vibe coding but end up in the same time sink traps as the study found. If the LLM is ever wrong, you save time forking the chat than trying to realign it, but it’s still likely to be slower. Repeat chats result in the same pitfalls for complex issues and bugs, so you have to abandon that state quickly.

Vibe coding small revisions can still be a bit faster and it’s great at helping me with documentation.