> Perhaps this is the part you're missing. When I've watched people program with ChatGPT it _is_ a lot of back and forth because an enormous amount of context is able to be stored and back referenced.
I've tried to build a lot of fun stuff with it so far. Haven't been able to properly 'program it in English' for anything non-trivial. Back and forth ended up in loops of not what I wanted. I'm just utterly confused at the difference in experiences I've had with it vs. what some people are preaching.
> There are obviously _huge_ limitations to this approach and professionals will get hit them fast, but the proof is in the pudding: people who can barely code are producing real software through this approach. It's happening.
I've had 4 product people I know try to create products using ChatGPT. All 4 of them basically got stuck on the first steps of whatever they were trying to do. "Where do I have to put this code?", "How do I put it online?", "How do I store user data?", "Where do I get a database from?". Basic questions to any professional, but to them it was impossible to overcome the obstacles from code to deployment.
I don't doubt that it's happening and it will become better in the future; I'm just having a hard time trying to grasp where some people are coming from when my experience as a professional, using it, has been mixed.
i've observed this schism between people who can get LLMs to produce useful output and people who are baffled, I think it's a mixture of two things:
expectations: using to the LLM to break problems into steps, suggest alternatives, using the LLM to help them think through the problem. I think this is the people using it to write emails - myself included, having a loop to dial in the letter allows me to write the letter without the activation energy needed to stare at a blank page
empathy: people who've spent enough time interacting with an LLM get to know how to boss it around. I think some people are able to put themselves in the LLMs shoes and imagine how to steer the attention into a particular semantic subspace where the model has enough context to say something useful.
GPT4 writes boilerplate python and javascript servers for me in one shot because I ask for precisely what I want and tell it what tools to use - I think because I have dialed in my expectation for what it's capable of and I learned how to ask in precise language, I get to be productive with GPT4's code output. Here's a transcript: https://poe.com/lookaroundyou/1512927999932108
I've tried to build a lot of fun stuff with it so far. Haven't been able to properly 'program it in English' for anything non-trivial. Back and forth ended up in loops of not what I wanted. I'm just utterly confused at the difference in experiences I've had with it vs. what some people are preaching.
> There are obviously _huge_ limitations to this approach and professionals will get hit them fast, but the proof is in the pudding: people who can barely code are producing real software through this approach. It's happening.
I've had 4 product people I know try to create products using ChatGPT. All 4 of them basically got stuck on the first steps of whatever they were trying to do. "Where do I have to put this code?", "How do I put it online?", "How do I store user data?", "Where do I get a database from?". Basic questions to any professional, but to them it was impossible to overcome the obstacles from code to deployment.
I don't doubt that it's happening and it will become better in the future; I'm just having a hard time trying to grasp where some people are coming from when my experience as a professional, using it, has been mixed.