When I search this topic online, I always find either wrong information or advertising lies. So what is actually something that LLMs can do very well, as in being actually useful and not just outputing a nonsensical word salad that sounds coherent.
Results
So basically from what I’ve read, most people use it for natural language processing problems.
Example: turn this infodump into a bullet point list, or turn this bullet point list into a coherent text, help me with rephrasing this text, word association, etc.
Other people use it for simple questions that it can answer with a database of verified sources.
Also, a few people use it as struggle duck, basically helping alleviate writers block.
Thanks guys.
Not exactly sure this is the “right way” to use them, but I use one as an autocomplete helper in my IDE. I don’t ask it to code anything, just use it as autocomplete.
Majority of the time, it works well, especially in common languages like Python.
Just rewrote my corporate IT policies. I feed it all the old policies and a huge essay of criteria, styles, business goals etc. then created a bunch of new policies. I have chatgpt interview me about the new policies, I don’t trust what it outputs until I review it in detail and I ask it things like
What do other similar themed policies have that I don’t? How is the policy going to be hard to enforce? What are my obligations annually, quarterly and so on?
What forms should I have in place to capture information ( i.e. consultant onboarding).
I can do it all myself but it would be slower and more likely to have consistency and grammatical errors.
Taking a natural language question and providing a foothold on a subject by giving you the vocabulary so that you can research a topic on your own.
“What is it called when xyz.”
A fringe case I’ve found ChatGPT very useful is to learn more about information that is plentiful but buried in dead threads in various old school web forums and thus very hard to Google. Like other people’s experiences from homebrewing. Then I ask it for sources and most often it is accurate to the claims of other homebrewers that also can be correct or less correct.
I use it to help me come up with better wording for things. A few examples:
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Writing annual goals for my team. I had an outline of what I wanted my goals to be, but wanted to get well written detail about what it looks like to meet or exceed expectations on each goal and to create some variations based on a couple of different job types.
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Brainstorming interview questions. I can use the job description and other information to come up with a starting list of questions and then challenge the LLM to describe how the question is useful. I rarely use the results as-is, but it helps me to think through my interview plan better than just using a list of generic questions.
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Converting a stream of thought bullet list into a well written communication.
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I ask it increasingly absurd riddles and laugh when it hallucinates and tells me something even more absurd.
I use it pretty sparsely, and it’s stuff that’s simple but if I were to google I’d get a whole 10 page essay about filled with ads.
For example here’s some of my recent searches, the span is like ~2 months back.
- XXL and 2X being the same thing.
- Rainbow Minecraft MOTD server text.
- Script to find all the schematic files in my ~30 nested files and copy them to a new folder
- how to get cat hair out of clothes
- some legal supreme court thing from the 1800s
- creative commons CC-BY-SA explanation (their website didn’t explain what the abbreviation of “BY” was)
- how to unlock my grandad’s truck with the code
- “can ghosts be black” (??? I think I was in a silly argument on discord)
- how to read blood pressure
- scene from movie where I forgot the movie
- how to draw among us in desmos calculator as a joke
I find they’re pretty good at some coding tasks. For example, it’s very easy to make a reasonable UI given a sample JSON payload you might get from an endpoint. They’re good at doing stuff like crafting farily complex SQL queries or making shell scripts. As long as the task is reasonably focused, they tend to get it right a lot of the time. I find they’re also useful for discovering language features working with languages I’m not as familiar with. I also find LLMs are great at translation and transcribing images. They’re also useful for summaries and finding information within documents, including codebases. I’ve found it makes it a lot easier to search through papers where you might want to find relationships between concepts or definitions for things. They’re also good at subtitle generation and well as doing text to speech tasks. Another task I find they’re great at is proofreading and providing suggestions for phrasing. They can also make a good sounding board. If there’s a topic you understand, and you just want to bounce ideas off, it’s great to be able to talk through that with a LLM. Often the output it produces can stimulate a new idea in my head. I also use LLM as a tutor when I practice Chinese, they’re great for doing free form conversational practice when learning a new language. These are a just a few areas I use LLMs in on nearly daily basis now.
I use LLMs to generate unit tests, among other things that are pretty much already described here. It helps me discover edge cases I haven’t considered before, regardless if the generated unit tests themselves pass correctly or not.
Oh yeah that’s a good use case as well, it’s a kind of a low risk and tedious task where these things excel at.
I have it make me excel formulas that I know are possible, but I can’t remember the names or makeup for. Afterwords I always ask “what’s a better way to display this data?” And I sometimes get a good response. Because of data security reasons I dont give it any real data but we have an internal one I can use for such things and I sometimes throw spreadsheets in for random queries that I can make in plain language.
I use it for helping me learn German but only for explaining things like grammatical rules, concepts, or word uses.
Do not ask it to translate or write something for you. It will make lots of grammatical mistakes. I find that it often misgenders or uses the wrong case for nouns in a sentence.
Very basic and non-creative source code operations. Eg. “convert this representation of data to that representation of data based on the template”
I find it’s really good for asking extremely specific code questions
They work well when being correct doesn’t matter
Well, yes, but then what’s the point? It would be like having Wikipedia filtered through Alex Jones.
There are plenty of use cases that don’t involve it needing to recite accurate facts.
I used it to help write copy for my website, to write proposals, and to help with rephrasing when I can’t think of the most diplomatic way to say a thing.
For instance: commenting on Reddit
JIRA queries, rules, automations, etc. Suggestions for how to make my rage-fueled communications sound more reasonable and professional Meeting Summaries. Not having to take notes is HUGE.
Meeting notes are the ideal use case for AI, in the sense that everyone thinks someone needs to write them but almost nobody ever goes back and actually reads them.
But when I got curious and read the AI generated ones (the ones from Zoom at least)… According to the AI I had agreed on an action that hadn’t been even discussed in the meeting and we apparently spent half of the meeting discussing weather conditions in the various locations (AI seems to have a hard time telling the difference between initial greetings or jokes and the actual discussion, but in this one it became weirdly fixated with those initial 5 minutes)
This is one area where, at least for me, CoPilot is very good. In most other areas, CoPilot is not very good.
kill time
