4 ms·

I'm with you (I use Claude Sonnet, but same difference...). I do wonder if we're the last generation that will be able to effectively do such "course correct"

I'm with you (I use Claude Sonnet, but same difference...). I do wonder if we're the last generation that will be able to effectively do such "course correct" operations -- feels like a good chunk of the next generation of programmers will be bootstrapped using such LLMs, so their ability to "have that insight" will be lacking, or be very challenging to bootstrap. As analogy, do you find yourself having to "course correct" the compiler very often?

kshacker 2y ago
Many a times. I asked it a simple non programming question. My last paycheck was December 20, 2024. I get paid biweekly. In which year will I get paid 27 times. It got it wrong ... very articulately. I run into this every single day.
- CharlesW 2y ago
  You'll be more successful with this the more you know how LLMs work. They're not "good" at math because they just predict text patterns based on training data rather than perform calculations based on logic and mathematical rules. To do this reliably, prepend your request to invoke a tool like OpenAI's Code Interpreter (e.g. "Code the answer to this: My last paycheck was December 20, 2024. I get paid biweekly. In which year will I get paid 27 times.") to get the correct response of 2027.
  - kshacker 2y ago
    Sure, thanks ! Your suggestion worked. I looked up my chat history and the following was my original question (my answer above was from memory) > I get paycheck every 2 weeks. Last paycheck was December 20, 2024. Which year will I have 27 paychecks? I sent it again and it bombed again. It seems your prompt and my prompt are quite similar, but I realize the suggestion (or direction) to it to code.
    - CharlesW 2y ago
      Awesome! I'm sure the following is not an original thought, but to me it feels like the era of LLMs-as-product is mostly dead, and the era of LLMs-as-component (LLMs-as-UX?) is the natural evolution where all future imminent gains will be realized, at least for chat-style use cases. OpenAI's Code Interpreter was the first thing I saw which helped me understand that we really won't understand the impact of LLMs until they're released from their sandbox. This is why I find Apple's efforts to create standard interfaces to iOS/macOS apps and their data via App Intents so interesting. Even if Apple's on-device models can't beat competitors' cloud models, I think there's magic in that union of models and tools.

kshacker 2y ago

Many a times. I asked it a simple non programming question. My last paycheck was December 20, 2024. I get paid biweekly. In which year will I get paid 27 times. It got it wrong ... very articulately. I run into this every single day.

CharlesW 2y ago
You'll be more successful with this the more you know how LLMs work. They're not "good" at math because they just predict text patterns based on training data rather than perform calculations based on logic and mathematical rules. To do this reliably, prepend your request to invoke a tool like OpenAI's Code Interpreter (e.g. "Code the answer to this: My last paycheck was December 20, 2024. I get paid biweekly. In which year will I get paid 27 times.") to get the correct response of 2027.
- kshacker 2y ago
  Sure, thanks ! Your suggestion worked. I looked up my chat history and the following was my original question (my answer above was from memory) > I get paycheck every 2 weeks. Last paycheck was December 20, 2024. Which year will I have 27 paychecks? I sent it again and it bombed again. It seems your prompt and my prompt are quite similar, but I realize the suggestion (or direction) to it to code.
  - CharlesW 2y ago
    Awesome! I'm sure the following is not an original thought, but to me it feels like the era of LLMs-as-product is mostly dead, and the era of LLMs-as-component (LLMs-as-UX?) is the natural evolution where all future imminent gains will be realized, at least for chat-style use cases. OpenAI's Code Interpreter was the first thing I saw which helped me understand that we really won't understand the impact of LLMs until they're released from their sandbox. This is why I find Apple's efforts to create standard interfaces to iOS/macOS apps and their data via App Intents so interesting. Even if Apple's on-device models can't beat competitors' cloud models, I think there's magic in that union of models and tools.