5 ms·

> For a good 12 hours, over the course of 1 1/2 days, I tried to prompt it such that it yields what we needed. Eventually, I noticed that my prompts converged m

by dgb23 1y ago

> For a good 12 hours, over the course of 1 1/2 days, I tried to prompt it such that it yields what we needed. Eventually, I noticed that my prompts converged more and more to be almost the code I wanted. After still not getting a working result, I ended up implementing it myself in less than 30 minutes. This is very common. We need a name for this phenomenon. Any analogies/metaphors? I generated (huh!) some suggestions and the funniest one seems to be: > Just One More Prompt Syndrome (JOMPS) But I assume there's a better one that describes or hints at the irony that describing precisely what a program should be doing _is_ programming.

rvnx 1y ago
Could be that some people are not familiar with the limitations of LLMs and what to expect from them, and could benefit from learning how to properly do prompting, before outright rejecting them. The LLMs are sometimes frustrating, but over time you learn how to drive them properly, exactly like when you learn how to use the right keywords on Google. Also, considering he is a free user, the author might be using one the default models ("auto" -> o4-mini or gpt-4o, rarely Claude Sonnet/Opus 3) in Cursor -> Both of them are rather weak, especially with the automatic context splitting that Cursor does to save money Claude Code + Opus 4 would likely yield better results here.
- rednafi 1y ago
  > Also, considering he is a free user, the author might be using one the default models ("auto" -> o4-mini or gpt-4o, rarely Claude Sonnet/Opus 3) in Cursor I wonder where you got the free user info.
  - rvnx 1y ago
    Possibly, though the context strongly suggests someone discovering Cursor. It's also possible that someone shared their account with him and that could explain the "30-minute thinking time" (the free slow requests).
- Chinjut 1y ago
  Just use the right prompt. Just use the right model. Just roll the dice correctly.
  - dinfinity 1y ago
    This is too dismissive. There is a massive difference in outputted code quality between models. The recent Google Gemini 2.5 models have dethroned the Anthropic ones. OpenAI o3 is the only other one that is even worth considering; all the rest of the OpenAI models are trash in comparison. In addition to that we've also seen that the way you prompt (amount of use of expert language), what context you provide through instructions, and tool use make a huge difference on the outcome. At this point, if your coding experience with LLMs sucks I'd say there is an 80% chance that you're just doing it wrong.
    - creesch 1y ago
      The differences can't be that massive given that the hype already made many of these promises well before these models were ever a thing. Basically you are just adding to it with "You just need to use the latest model, anything before that is trash". Ignoring that the same was said just a few months prior when those models were cutting edge.
      - dinfinity 1y ago
        > The differences can't be that massive given that the hype already made many of these promises well before these models were ever a thing. I'm not talking about other people's incorrect promises _and_ I mentioned a number of things in which proper usage today is different from what people were doing before. > Basically you are just adding to it with "You just need to use the latest model, anything before that is trash". That's not what I said. Don't put words into my mouth. I said that the older models are trash in comparison, not that they are trash. The older models require more work in prompting to get decent results. > Ignoring that the same was said just a few months prior when those models were cutting edge. You are conveniently ignoring the other parts I mentioned to make my claim. Here: "In addition to that we've also seen that the way you prompt (amount of use of expert language), what context you provide through instructions, and tool use make a huge difference on the outcome."
RhysU 1y ago
> We need a name for this phenomenon. There's a classical one: Sunk cost fallacy
Izkata 1y ago
Based on social media's "doomscrolling", how about "doom prompting"?
- dgb23 1y ago
  I like "doom prompting", it feels exactly like that! One effective use of these tools is to get past the blank page dread. Let it write _something_ to get going, then turn it off and start working. Recognizing where this cutoff should be can prevent "doom prompting"!

by dgb23 1y ago

rvnx 1y ago

Could be that some people are not familiar with the limitations of LLMs and what to expect from them, and could benefit from learning how to properly do prompting, before outright rejecting them. The LLMs are sometimes frustrating, but over time you learn how to drive them properly, exactly like when you learn how to use the right keywords on Google. Also, considering he is a free user, the author might be using one the default models ("auto" -> o4-mini or gpt-4o, rarely Claude Sonnet/Opus 3) in Cursor -> Both of them are rather weak, especially with the automatic context splitting that Cursor does to save money Claude Code + Opus 4 would likely yield better results here.

rednafi 1y ago
> Also, considering he is a free user, the author might be using one the default models ("auto" -> o4-mini or gpt-4o, rarely Claude Sonnet/Opus 3) in Cursor I wonder where you got the free user info.
- rvnx 1y ago
  Possibly, though the context strongly suggests someone discovering Cursor. It's also possible that someone shared their account with him and that could explain the "30-minute thinking time" (the free slow requests).
Chinjut 1y ago
Just use the right prompt. Just use the right model. Just roll the dice correctly.
- dinfinity 1y ago
  This is too dismissive. There is a massive difference in outputted code quality between models. The recent Google Gemini 2.5 models have dethroned the Anthropic ones. OpenAI o3 is the only other one that is even worth considering; all the rest of the OpenAI models are trash in comparison. In addition to that we've also seen that the way you prompt (amount of use of expert language), what context you provide through instructions, and tool use make a huge difference on the outcome. At this point, if your coding experience with LLMs sucks I'd say there is an 80% chance that you're just doing it wrong.
  - creesch 1y ago
    The differences can't be that massive given that the hype already made many of these promises well before these models were ever a thing. Basically you are just adding to it with "You just need to use the latest model, anything before that is trash". Ignoring that the same was said just a few months prior when those models were cutting edge.
    - dinfinity 1y ago
      > The differences can't be that massive given that the hype already made many of these promises well before these models were ever a thing. I'm not talking about other people's incorrect promises _and_ I mentioned a number of things in which proper usage today is different from what people were doing before. > Basically you are just adding to it with "You just need to use the latest model, anything before that is trash". That's not what I said. Don't put words into my mouth. I said that the older models are trash in comparison, not that they are trash. The older models require more work in prompting to get decent results. > Ignoring that the same was said just a few months prior when those models were cutting edge. You are conveniently ignoring the other parts I mentioned to make my claim. Here: "In addition to that we've also seen that the way you prompt (amount of use of expert language), what context you provide through instructions, and tool use make a huge difference on the outcome."

RhysU 1y ago

> We need a name for this phenomenon. There's a classical one: Sunk cost fallacy

Izkata 1y ago

Based on social media's "doomscrolling", how about "doom prompting"?

dgb23 1y ago
I like "doom prompting", it feels exactly like that! One effective use of these tools is to get past the blank page dread. Let it write _something_ to get going, then turn it off and start working. Recognizing where this cutoff should be can prevent "doom prompting"!