My “Perfect” prompt broke overnight, and it was a masterclass in why context matters.

I finally did it. Last week, I built a prompt that generated a flawless documentation site from a GitHub repo. It was beautiful. I felt like a wizard. I even bookmarked it as my “Gold Standard” prompt.

Then, yesterday happened.

I ran the exact same prompt on a new repo—similar structure, similar size—and it was a total disaster. The AI started ignoring the CSS requirements, forgot to link the sub-pages, and kept trying to write the docs in a weird, conversational tone I never asked for.

I spent four hours “patching” the prompt. I added bold text, CAPITAL LETTERS, and triple-exclamation points telling it to STAY ON TASK. Nothing worked. I was about to blame a model update or some back-end tweak.

The Realization:

I stepped back and looked at the two repos side-by-side. The first repo had very descriptive function names; the second repo was more abstract. The AI wasn’t “getting worse”—it was getting lost in the ambiguity of the source material. My prompt relied on the model guessing the context instead of me defining it.

The Fix:

I stripped the prompt back to basics. Instead of telling it to “Be a Technical Writer,” I gave it a specific Markdown Template and told it: “Your only job is to fill this template using the provided AST (Abstract Syntax Tree) logic. If a variable is unclear, mark it as ‘TBD’ rather than guessing.”

By removing the “creative freedom” I thought I needed, I gained the consistency I actually required.

It’s a tough pill to swallow, but I realized that a “perfect prompt” doesn’t exist if it can’t handle messy context. I’ve started moving away from “Instructional Prompting” toward “Template-Driven Prompting.”

Has anyone else had their “Go-To” prompt fail them out of nowhere? How do you guys handle testing your prompts across different datasets to make sure they’re actually robust?