Ask HN: Become a 10x developer with LLM? Myth? Reality?

> But it regurgitates Stackoverflow with sub 100% accuracy, and then you need to spend time investigating in what went wrong/trying to fix it

Since it is bespoke boilerplate, it isn't "just" documentation or Stackoverflow. Take this example, I gave it a library, I gave it custom names for everything, and the result is pretty time-saving:

Prompt:

     Create html using the bootstrap 5 library, create form with a fieldset (title: "Filter") inside with the following select option/label groups: 
      - Department    
      - Year 
      - Invoice Source 
      - Invoice Destination 
     
     Then two inputs/labels for start date and end date, and a submit button. Only include one example in each of the select options and no page boilerplate.

Result:

       
       
         Filter
         
           Department
           
             Choose department
             Sales
           
         
         
           Year
           
             Choose year
             2022
           
         
         
           Invoice Source
           
             Choose source
             Email
           
         
         
           Invoice Destination
           
             Choose destination
             Customer
           
         
         
           
             Start Date
             
           
           
             End Date
             
           
         
         
           Filter
         
       
     

I didn't believe it either until I met one. They exist, but it takes more than an interview to gauge and they're so rare it's not worth trying.

I pay for ChatGPT-4. It's helping me program noticeably better and much faster, even with mid-sized projects involving many files. This is the process that I follow:

- I write a system prompt with a succinct description of what I want to implement: "X is an online app that does Y, with these features: ..." I try to be exhaustive, and I write as I would if I were describing what I want to do to a very proficient programmer who needs to understand the problem and the solution. I save this to a prompts/0-system.txt file that will be part of the project.

- I design an architecture and define the general boundary conditions. I may ask ChatGPT for opinions, but at this stage, it's mostly based on experience. I add it to the system prompt.

- I write a description of the first chunk of functionality that I want to implement, usually a set of functions or a class, at the individual file level. For example, prompts/1-dal.txt.

- I (actually ChatGPT) wrote a very simple recursive cat script (https://github.com/juanre/catprompt) that combines several files into a single prompt (a file can include other files, etc). I add a line to prompt/1-dal.txt pointing to the system prompt, and use catprompt to create a combined prompt that will include the overall description and the specifics of the functionality I am after.

- I give this prompt to ChatGPT, and it produces the code. It's typically perfectly fine. I add it to the project, for example, as dal.py, review, and fix whatever may need fixing.

- I ask ChatGPT to create tests in the same session. This also tends to work fine.

- Then I move to the next piece of functionality, in a new session. I write another description, in another file, including the system prompt and usually the code already created.

The prompts remain: they become a description of the code (and of my intentions) for other programmers, and they can help ChatGPT write documentation as well.

I enjoy programming like this. The effort I make in writing concise descriptions helps me think more clearly. I used to love literate programming back in the 90s, and this feels very much like it. Knuth described it thus:

"Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do."

Replace "human beings" with LLMs, and I think that's where we are now.

I would say that since LLMs made it on the scene my code output efficiency went 2-3x. As a backend dev I ended up implementing several frontend features at work, stuff I would have not touched previously. I did not blindly implement them. But had ChatGPT teach me how to do so. But I would say that I could still not fully understand it without ChatGPT. And as for debugging and googling things goes. Efficiency has definitely gone up 5x in many cases. Maybe for some cases as much as 10-20x.

However my overal efficiency and productivity maybe only went up maybe 2-3x? I don’t know. It’s hard to assess. Writing code is really only part of the job, so it can only do so much. But I am absolutely sure that even now there will be outliers who went from being 1x developers to the mythical 10x, based on the improvements I have seen with myself.

"Reality. ChatGPT has been working fine for me but I have a ton of programming experience so I can easily fix any mistakes. I will be upgrading to plus soon.'

I think this is the reality of the current crop of LLMs. It's like having and assistant that you need to constantly check on. If you have lots of experience you can quickly catch the problems and move on but if you don't then it slows you down. My feeling is that in every field that it influences it will make experts more productive by reducing the tedious parts but for novice it will help but they won't have a 10x increase in productivity.

My general AI view is that to get the most of it you better become an expert in your field so you can catch problems with the output fast. Those that don't or can't will have a tough future trying to compete in the job market.