I haven’t had any luck prompting LLMs to “have taste.” They seem to over fixate on instructions (e.g. golfing when asked for concise code) or require specifying so many details and qualifications that the results no longer generalize well to other problems.

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

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johndough a few seconds ago | parent | next [-]

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.

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zarzavat 3 hours ago | parent | prev | next [-]

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.

	▲	kqr 2 hours ago \| parent \| next [-]
		Makes me think of this commitstrip comic: https://i.xkqr.org/itscalledcode.jpg (mirrored from the original due to TLS issues with the original domain.) A guy with a mug comes up to a person standing with their laptop on a small table. The mug guy says, "Some day we won't even need coders any more. We'll be able to just write the specification and the program will write itself." Guy with laptop looks up. "Oh, wow, you're right! We'll be able to write a comprehensive and precise spec and bam, we won't need programmers any more!" Guy with mug takes a sip. "Exactly!" Guy with laptop says, "And do you know the industry term for a project specification that is comprehensive and precise enough to generate a program?" "Uh... no..." "Code. It's called code."
	▲	FeepingCreature an hour ago \| parent \| prev [-]
		the goal would be to write it a reusable prompt. this is what AGENT.md is for.

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SerCe 2 hours ago | parent | prev [-]

> 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.

	▲	tobr 2 hours ago \| parent [-]
		That’s an interesting approach, but what do you learn from it that is applicable to the next task? Do you find that this eventually boils down to heuristics that generalize to any task? It sounds like it would only work because you already put a lot of effort into understanding the constraints of the specific problem in detail.