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[ARTICLE · art-136003] src=dotnetperls.com ↗ pub= topic=ai-tools verified=true sentiment=↑ positive

Maintaining Prompts Instead of Code

A developer reported that a local model, Ternary Bonsai 2, generated the parse_prompt function returning ParsedPromptInfo from a 180-line Markdown specification in about 30 minutes, producing 300-400 lines of Rust with 3 small compile-time errors that were fixed. The developer now plans to maintain the prompt Markdown file rather than the generated Rust code, regenerating it with models such as GPT-6 Astra or Claude Fable when optimizations or new features are needed.

read1 min views1 publishedSep 21, 2026

So I wrote a 180-line Markdown file containing a detailed specification of a parsing function needed in my program. I used a local model (Ternary Bonsai 2) to generate the parse_prompt function (which returns ParsedPromptInfo) in about 30 minutes.

The code was mostly correct, although there were 3 small compile-time errors that I fixed. Now my plan is to maintain the prompt file instead of the Rust code. This means:

• If I want to optimize the code, I could buy tokens and have GPT-6 Astra or Claude Fable follow the prompt from scratch (I could add a note that I want performance optimizations).

• If I want a different feature, I could just change the prompt file and then have a model rewrite the code.

For now, the first iteration from Bonsai was enough for my needs—it was 300-400 lines of Rust. As the entire file is just generated code, I should not maintain it directly—it would be better for me to just maintain the prompt Markdown file.

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