Prompt Engineering Isn't a Job Title, It's a Skill Every Product Needs Now

"Prompt engineer" had a brief moment as a standalone job title, and the more accurate framing that's held up is that writing clear, well-structured instructions for a language model is a skill that belongs inside product and engineering work generally, not a separate specialized role sitting apart from it.

What actually makes a prompt reliable, in practice

• Specificity about the exact output format expected, not just the general task — a model given a clear structure to fill produces far more consistent results than one given an open-ended request.
• Explicit boundaries on what the model should and shouldn't do, since the gaps in an instruction get filled with the model's own assumptions, which won't always match yours.
• Concrete examples of good output included directly in the prompt, which reliably improves consistency more than a longer written description of what "good" means.
• Testing against genuinely difficult, adversarial inputs, not just the clean happy-path examples that always work regardless of prompt quality.

Why this belongs in the same conversation as any other engineering skill

A poorly-specified prompt produces inconsistent, unreliable output the same way a poorly-specified function signature produces bugs — it's a real design skill, and treating it as a mysterious separate discipline understates how much it benefits from the same rigor behind scoping any AI agent carefully.

A vague prompt gets a plausible-sounding, unreliable answer. A specific one gets a consistent, checkable one. The model didn't get smarter between those two prompts — the instructions did.

This is a skill we build directly into every AI feature we ship now, refined iteratively against real inputs, rather than treated as a one-time setup step — the same discipline we apply to avoiding vague AI feature requests generally.