Almost every "AI is overrated" complaint, when you trace it back, is really a prompt problem. The model is doing exactly what you told it to do. The trouble is what you told it.
Here is the loop most people are stuck in:
- Write a one-sentence prompt.
- Get a generic answer.
- Conclude AI is mid.
- Repeat.
The fix is simple but unfamiliar: the model is averaging across all the cases your prompt could have meant. If your prompt could mean ten things, you get the average of ten things. That's the generic answer. The way out is to make your prompt mean only one thing.
A prompt generator does this for you by asking the questions whose answers narrow the meaning. But you can also do it by hand once you understand what specifically is going wrong.
The four reasons prompts produce generic answers
Almost every generic answer traces back to one of these four root causes.
1. No audience specified
"Write me an email about our product launch."
Email to whom? A free-tier user is a different person from a paying customer is a different person from a journalist. Each gets a different email. Without an audience, the model writes the email that minimizes how wrong it is across all three — which means it lands on the most generic surface possible.
Fix: Always name the audience in the first sentence of context. "Existing free-tier users at mid-market companies" beats "potential customers."
2. No outcome specified
"Summarize this document."
Summarize for what purpose? To brief a CEO who has 90 seconds? To onboard a new engineer who needs to be operational tomorrow? To pitch the product to a journalist? Same document, three different summaries. Without an outcome, you get the lowest-common-denominator summary.
Fix: State the decision the answer will inform. "Summarize so a CEO can decide whether to fund the project this quarter" produces a different summary than "summarize so an engineer can implement it."
3. No constraints
"Write me copy for the landing page hero."
How long? What tone? Avoid any specific words? Include any specific facts? With no constraints, the model produces the most generic landing page copy it has read — exclamation marks, "Welcome to the future," promises about transformation. You hated that copy when other companies wrote it; you hate it more when it's yours.
Fix: State 3-5 explicit constraints. Length, tone, words to use, words to never use. Constraints are not creative limits; constraints are how the model knows what kind of answer you want.
4. No success criterion
"Write me a PRD."
When is the PRD done? When is it good? Without a success criterion, the model stops when it feels done, which is usually too early or way too late. And it has no way to evaluate its own draft.
Fix: State at least one success criterion. "The PRD is good if every user story has at least 3 acceptance criteria" or "the PRD is good if a senior engineer could estimate it without follow-up questions" both give the model a way to self-check before stopping.
A diagnostic question you can ask yourself
Next time the model gives you a generic answer, ask: "If I gave this prompt to ten different people who all knew the topic, would they produce roughly the same response?"
If yes — you have a great, specific prompt and the model just messed up.
If no — the ten people would produce ten different answers, depending on how they interpreted your prompt. The model picked the average. That's not the model's fault. That's the prompt.
This is why a prompt generator helps even people who already know how to write prompts: it forces the diagnostic step. By identifying gaps in your input and asking ranked clarifying questions, it ensures every prompt that leaves the workspace has only one valid interpretation. The generic-answer trap closes.
A worked diagnosis
You wrote: "Help me prepare for a difficult conversation with a teammate."
The model wrote: a generic 12-bullet list about active listening, "I" statements, and finding common ground.
Run the four-question diagnostic:
- Audience? Missing. The teammate is a person, but you didn't say what their personality, role, or relationship to you is.
- Outcome? Missing. Are you trying to fire them? Coach them? Resolve a project conflict? Understand why they ghosted you?
- Constraints? Missing. Length, tone, what to avoid (e.g. "no HR-speak"), what role you're playing.
- Success criterion? Missing. How will you know the prep was good?
A prompt that fixes all four:
You are an experienced manager who has run 200+ difficult conversations. Help me prepare for a 30-minute conversation with a senior engineer who keeps missing 1:1s and may be quiet-quitting. They report to me. I have hired them but not promoted them. I want them to either re-engage or leave amicably. Constraints: no HR-speak, no "let's circle back," no role-play scripts. Output format: 3 sections — opening (one paragraph), 5 questions to ask in order, 3 contingency responses for likely answers. Success criterion: I should leave this prep feeling I know exactly how to start the conversation and what I'm trying to achieve.
That prompt produces something useful. The original produced 12 bullets you could have written yourself.
The shortcut
You can do this analysis manually for every prompt you write. Or you can let a prompt generator do it: it spots the gaps, asks you only the questions that close them, and assembles the structured prompt for you. Same outcome, less mental tax.
The deeper point is that "AI gives generic answers" was always backwards. AI gives the answer that matches the prompt. The fix is upstream of the model. And once you start writing prompts that mean only one thing, you stop seeing AI as mid — because the answers you get start matching the work you actually wanted done.