I was trying to do a memory test to see how far back 3.5 could recall information from previous prompts, but it really doesn’t seem to like making pseudorandom seeds. 😆

  • Glide@lemmy.ca
    15·
    7 months ago

    I use it as a brainstorming tool. I haven’t had a single question make it as-is to a student’s worksheet. If the tool can’t even count to 20 successfully, I’m not sure how anyone could trust it to generate meaningful questions for an ELA program.

      • millie@beehaw.orgOPEnglish
        4·
        7 months ago

        I haven’t had much luck with it writing stuff from scratch, but it does a great job of helping with debugging and figuring out why complex equations are doing what they’re doing.

        I put together a pretty complex shader recently, and gpt 3.5 did a great job of helping me figure out why it wasn’t doing quite what I wanted.

        I wouldn’t trust it to code anything without my input, but it’s great for advice and explanations and certain kinds of problem solving. Just don’t assume it has the right answer, you still have to do the work

        • jarfil@beehaw.org
          1·
          7 months ago

          I’ve tried it with languages I don’t know, and it managed to write simple working functions by just iterating over:

          1. Ask it to write the code
          2. Try to run the code, write down any errors
          3. Look up the errors, and ask it to fix them in the code
          4. Repeat from 2 until there are no more errors

          It seems to lose context easily, like if you ask it to fix one error, then another, it might revert the first fix, but asking it to fix both at once, tends to work.

          I think someone could feasibly write several working functions or modules, without knowing much about a given language, as long as they are clear about what they want them to do… but of course spotting obvious errors and fixing them by hand, can be faster. Fixing integration problems is where I think it might get harder (haven’t tried though, could be interesting).

      • Hexarei@programming.dev
        1·
        7 months ago

        Well, it’s terrible at factual things and counting, and even when it comes to writing code it will often hallucinate APIs and libraries that don’t exist - But when given very limited-scope, specific-domain problems with enough detail and direction, I’ve found it to be fairly competent as a rubber ducky for programming.

        So far I’ve found ChatGPT to be most useful for:

        1. Writing SQL. Seriously, it’s fantastic at writing SQL if you tell it the relevant schema and what you’re trying to achieve.
        2. Brainstorming feature flow - Tell it the different parts of a feature, ask for thoughts on how the user should be guided through the process, and it does a decent job of suggesting ideas.
        3. Generating alternative names/labels for buttons and such. “In X feature, I have a button that does Y when the user has Z. Currently I have that button labelled ‘Start Y’, but it feels robotic and impersonal. List 10 suggestions for what such a button could say to be more personal and friendly.” and the like. My favorite was a button that was labelled “Map Incoming Data to Job Details”. Wound up renaming the whole process to just “Job Ingestion” because it sounded so good.
        4. Reformatting data. Give it a data structure and tell it you want that data in some other data structure, and it is really accurate at reformatting it. I don’t think I’d trust it with a huge amount of data that way, but for an unimportant one-off it was a nice time savings.