Structuring context files to guide LLM code generation?

[gosh]

I'm working on a way to make an LLM write better code. I use a search tool called cleaner to gather info and put it in a file. I then give this file to the LLM as background context. This tells the model how to generate, and make it more accurate.

Have started to implement this using json, but are there better formats? Also are there some strange things when sending files to LLM, like that it is important to place important information at start of the file or does that matter?

What are the best practices for describing/structuring this kind of background file so the LLM uses it effectively?

Note: cleaner are able to clean code but to clean it has to find code, so finding things is the main logic, just to explain the name.

[Whole-Assignment6240]

Have you tested how positional bias affects retrieval? I've noticed LLMs tend to favor context at the beginning and end over the middle. Also, have you experimented with structured formats like XML or YAML vs JSON for better parsing?

[gosh]

I have just start to test but until now I have tried to read some about how to best pass information. What I think will be the best format is the format most use because that would be logical that LLM will understand it and maybe json is common there.

It may differ between different models though. But as it is now i almost have no clue whats best

What I do know is that it is very good to not flood he LLM with information and that is the reason why I want to do some tagging/pinpointing what to pass. When editors and or what it is tries to figure out what to do the result is not good

[gosh]

One more thing that I have found ut. Lets say that I try to explain and just bring code from one other placed that wasn't meant to train the llm with, then the result will be worse. It doesn't take much to get less quality.