What We Told You, and What 224 People Did Anyway
Three posts on this blog argue that prompt engineering is overrated: that structured prompt formats and template libraries are a distraction from the work that actually matters. We stand by that argument. We are not retracting it. What we had not done, until we read our own usage data, was check it against what our users actually do here. Grouped across every brief sent through Briefing Fox in six months: “AI and prompt engineering” is 224 briefs, ahead of business strategy, ahead of every academic use case, ahead of all image and video generation combined. Inside the developer category alone it shows up again at 10.5% of everything developers ask for. That is not a fringe behaviour we can wave off. It is the second most common thing this product is used for, running directly against what three of our own articles say people should be doing instead.The Category That Fails Three Times as Often
Here is the number that resolves the contradiction rather than deepening it.| Category | Briefs | Abandoned | Rate |
|---|---|---|---|
| Text and Logic | 1,065 | 107 | 10.0% |
| Developer and Coding | 384 | 66 | 17.2% |
| Image Generation | 94 | 16 | 17.0% |
| Agentic AI and Workflows | 72 | 24 | 33.3% |
Templates and Briefs Are Not the Same Move
This is the distinction the three earlier posts on this blog were gesturing at without quite landing on it: prompt templates fail for the same reason every borrowed framework fails. They were built for someone else’s agent, someone else’s task, someone else’s edge cases. Filling in the blanks does not add the one thing a working agent prompt actually needs, which is a clear, specific account of what this particular agent is for and what it is allowed to assume. A brief is different from a template in exactly one respect that matters: a brief starts from your situation and asks what an agent built for it would need to know. A template starts from a stranger’s situation and asks you to adapt. Adapting someone else’s answer is slower and worse than answering the question yourself, and it is the reason a category built entirely around specifying AI behaviour loses a third of the people who try it.What a Briefed Agent Prompt Has That a Template Never Will
An agent instruction copied from a library carries someone else’s assumptions about scope, tone, error handling, and what “done” looks like. None of those assumptions are stated, because the template’s author never met your task. When the agent behaves oddly, there is nothing in the prompt to diagnose, because nothing in it was actually decided by anyone thinking about your case. A briefed prompt states those things because someone was made to answer them before writing a word. What should this agent refuse to do. What does it need to know that is not obvious from the task name alone. What happens when it is uncertain. Where does it stop and hand back to a person. Those are not stylistic additions. They are the actual content of a working agent instruction, and a template has no way to contain them because it was written before your agent existed.Before You Write Your Next Agent Prompt
Skip the library. Before you write the instruction, answer four questions in plain language: what is this agent actually for, what should it never do, what does it need to know that you have not written down anywhere else, and what should happen when it hits something it cannot handle. That is a brief, not a template, and it is the difference between the 66.7% of agent-prompt attempts that finish and the third that do not. Try Briefing Fox free at briefingfox.comNo. Our own usage data shows prompt and agent engineering is the second largest use case on our platform, 224 of 1,678 real briefs. What fails is treating a prompt template as a shortcut around specifying your own agent. The activity is not the problem. The borrowed-template method is.
In our data, the Agentic AI and Workflows category abandons at 33.3%, three times the rate of ordinary text requests. Specifying what an agent should do, refuse to do, and handle when uncertain is a harder problem than writing a document, and a generic template has no way to answer those questions for your specific case.
A template was written for a hypothetical agent in a hypothetical situation and asks you to adapt it. A brief starts from your specific agent and asks what it needs: its scope, what it should refuse, what it needs to know, and what it should do when uncertain. Those answers cannot be borrowed because they depend on a task the template’s author never saw.
Four questions, in plain language: what is this agent actually for, what should it never do, what does it need to know that is not obvious from the task name, and what happens when it hits something it cannot handle. Answering those first is what turns a template into a working instruction.