Why generic prompts fail when generating documentation

I spent an entire afternoon trying to get an LLM to write API documentation for a payment gateway. The first three outputs were full of summaries and fake endpoint URLs that looked professional but were completely useless.

The breakthrough happened when I stopped asking for text and fed the model raw JSON payloads along with three specific error response codes. Giving it constraints forced it to write accurate reference guides instead of high-level marketing fluff.

Documentation turnaround time went from four hours down to twenty minutes per release. The secret is treating the model like a technical editor rather, than an open-ended creative writer.

How do you structure your input prompts to get technical output from generative AI models?