Yeah prompting doesn't work for this problem because the entire point of an LLM is you give it the what and it outputs the how. The more how that you have to condition it with in the prompt, the less profitable the interaction will be. A few hints is OK, but doing all the work for the LLM tends to lead to negative productivity.

Writing prompts and writing code takes about the same amount of time, for the same amount of text, plus there's the extra time that the LLM takes to accomplish the task, and review time afterwards. So you might as well just write the code yourself if you have to specify every tiny implementation detail in the prompt.

> Do you have any examples or resources that worked well for you?

Using this particular example, if you simply paste the exact code into the prompt, the model should able to reproduce it. Now, you can start removing the bits and see how much you can remove from the prompt, e.g. simplify it to pseudocode, etc. Then you can push it further and try to switch from the pseudocode to the architecture, etc.

That way, you'll start from something that's working and work backwards rather than trying to get there in the absence of a clear path.

What worked for me was Gemini 3 Pro (I guess 3.1 should work even better now) with the prompt "This code is unnecessarily complicated. Simplify it, but no code golf". This decreased code size by about 60 %. It still did a bit of code-golfing, but it was manageable.

It is important to start a new chat so the model is not stuck in its previous mindset, and it is beneficial to have tests to verify that the simplified code still works as it did before.

Telling the model to generate concise code did not work for me, because LLMs do not know beforehand what they are going to write, so they are rarely able to refactor existing code to break out common functionality into reusable functions. We might get there eventually. Thinking models are a bit better at it. But we are not quite there yet.

I have a stupid solution for this which is working wonders. It does not help to tell the LLM "don't do this pattern". I literally make it write a regex based test which looks for that pattern and fails the test.

For example I am developing a game using GDscript, LLMs (including codex and claude) keep making scripts with no classnames and then loading them with @preload. Hate this, and its explicitly mentioned in my godot-development skill. What agents can't stand is a failing test. Feels a bit like enforcing rules automatically.

This is a stupid idea but it works wonders on giving taste to my LLM. I wonder if I should open source that test suite for other agentic developers.

I really should spend some time analyzing what I do to get the good output I get..

One thing that is fairly low effort that you could try is find code you really like and ask the model to list the adjectives and attributes that that code exhibits. Then try them in a prompt.

With LLMs generally you want to adjust the behavior at the macro level by setting things like beliefs and values, vs at the micro level by making "rules".

By understanding how the model maps the aspects that you like about the code to language, that should give you some shorthand phrases that give you a lot of behavioral leverage.

Edit: Better yet.. give a fresh context window the "before" and "after" and have it provide you with contrasting values, adjectives, etc.

Concise isn't specific enough: I've primed mine on basic architecture I want: imperative shell/functional core, don't mix abstraction levels in one function, each function should be simple to read top-to-bottom with higher level code doing only orchestration with no control flow. Names should express business intent. Prefer functions over methods where possible. Use types to make illegal states unrepresentable. RAII. etc.

You need to think about what "good taste " is to you (or find others who have already written about software architecture and take their ideas that you like). People disagree on what that even means (e.g. some people love Rails. To me a lot of it seems like the exact opposite of "good taste").

I spend much more time refactoring that creating features (though, it is getting better with each model). My go-to approach is to use Claude Code Opus 4.6 for writing and Gemini 3.1 Pro for cleaning up. I feel that doing it just one-stage is rarely enough.

A lot of prompts about finding the right level of abstraction, DRY, etc.

An earlier example (Opus 4.5 + Gemini 3 Pro) is here: https://github.com/stared/sc2-balance-timeline

I tried as well to just use Gemini 3 Pro (maybe the model, maybe the harness) it was not nearly as good as writing, but way better at refining.

I actually don’t think golfing is such a bad thing, granted it will first handle the low hanging fruits like variable names etc, but if you push it hard enough it will be forced to think of a simpler approach. Then you can take a step back and tell it to fix the variable names, formatting etc. With the caveat that a smaller AST doesn’t necessarily mean simpler code, but it’s a decent heuristic.

Have you tried meta-prompts e.g. "Rewrite the prompt to improve the perceived taste and expertise of the author"