The LLMs are much more eager to please and to write lots of code. When I was younger, I would get distracted and play computer games (or comment on HN..), rather than churn out mountains of mediocre code all the time.
> The LLMs are much more eager to please and to write lots of code.
My process right now when working LLMs is to do the following:
- Create problem and solution statement
- Create requirements and user stories
- Create architecture
- Create skeleton code
- Map the skeleton code
- Create the full code
At every step, where I don't need the full code, the LLM will start coding and I need to stop it and add "Do not generate code. The focus is still design".
one of my biggest issues with the LLM is how it always wants to give me a mountain of code. A lot of the time Im using it for react, and it always gives me a full component no matter how much I specify I just want the method. It will not remember this for more than one message and will go back to giving me as much code as possible.
Yeah, it almost feels like you are talking to somebody with OCD. The frustrating part is, output tokens are usually a lot more expensive than input tokens, so they are wasting energy and money :-). Also, the more they generate, the greater the chance it will create attention issues as the conversation progresses.
This is why I built my chat app to let me manipulate LLM responses. If I feel it is not worth knowing, I'll just erase parts of it to ensure the conversation doesn't get side tracked. Or I will go back to the original user message and modify it to say
### IMPORTANT
- Do not generate more code than required.
The nice thing about LLM conversations are, every time you chat, the LLM treats it as a first time conversation, so this trick will work if the model is smart enough.
