⚡Spec-Driven Development in Legacy Code
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A prompt might contain precise instructions but make a false assumption.
It might ask for a solution before the problem is understood.
Or it might push the AI to agree with what you already believe.
Models tend to do this anyway. Researchers call it sycophancy: the model leans towards the answer it thinks you want.
The result can be an answer that sounds convincing, looks professional, and solves the wrong problem.
1. Don’t use vague quality words when you can name the criteria
❌ Asking for “rigor” without saying what rigor means
“Act as a senior software architect and give me a rigorous review of this design.”
What’s wrong?
Words like senior, rigorous, and high-quality sound useful, but they don’t say what the AI should examine or how it should back up its conclusions.
A response can sound authoritative without naming a single problem you can act on.
A role can set a perspective, but it doesn’t replace a clear task. (Asking the model to reason step by step before answering is different — that does change how it works on the problem. “Be rigorous” doesn’t.)