Fresh drop on r/PromptEngineering: a solo builder (u/clavam) shipped a prompt library with 51 categories, all sorted and rated by use case. The post is titled “Kinda gave up but still want to give it one last try,” which tells you where their head is at. Let’s give it a fair look, because small builds like this often hide the most useful ideas.
What’s new
The library is the shelf. The real product is what they call the Prompt Architect. You type the subject you want a prompt for, and it gives you two paths:
- Detailed mode: it asks you 5 simple questions to understand what you need.
- Skip and generate: no questions, fast prompt.
That’s it. No account wall mentioned, no pricing pitch. Just a Vercel-hosted tool and a plea for honest feedback. The 51 categories also matter more than they look. Sorting by use case means you can browse for something like “sales outreach” or “code review” instead of staring at a blank box, and the ratings give you a quick signal about which prompts other people found worth keeping. If you have ever started a prompt from zero and wasted ten minutes on wording, you know why a shelf like this saves time.
The twist
The interesting part isn’t the tool. It’s what the comments found. The builder says the use case is there but they can’t find the right users. Meanwhile, the commenters quietly pointed at the real design question: the skip button.
One person said the skip option feels like a trap. They keep skipping, then regret it. Another said a similar picker that asked just a couple of clarifying questions gave better output than instant-generate, because the extra context pushed the model in a better direction. And one skeptic argued you could just have this same conversation inside your LLM client and let it write the prompt for you.
So the twist is this. The feature people want most is the one that makes the tool slower. The questions are the product. The skip button is the temptation. Think about why that happens. A model can only work with what you give it. When you skip, it fills the gaps with safe, generic guesses about your audience, your format, and your tone. When you answer, those guesses turn into facts. Five short answers can be the difference between a prompt that sounds like everyone else’s and one that sounds like yours.
Mini-workflow: test it like a builder
Here’s how I’d run it in about five minutes:
- 🎯 Pick a real task you did this week, something like a cold email, a code review, or a content brief. Real tasks beat made-up ones because you already know what a good result looks like.
- 🐢 Run it in detailed mode first. Answer the 5 questions honestly and save the output. Resist the urge to be vague just to get through it faster.
- ⚡ Run the same subject with skip and generate. Compare the two side by side, and note how much generic filler shows up in the fast version.
- 🧪 Paste both prompts into your model of choice and judge the actual outputs, not the prompts. A prompt that looks longer is not automatically better. Only the result counts.
If detailed wins by a mile, you’ve learned the questions are doing the work. If the two come out about equal, the skeptic has a point, and your LLM client might be all you need. Either answer is useful, and both take less time than reading the comment thread.
Pro tips
- Steal the idea even if you skip the tool. Those 5 questions are basically a mini-brief: goal, audience, format, constraints, tone. Ask your own LLM to interview you with those before it writes anything. A simple starter line works well: “Before you write this, ask me five questions about my goal, audience, format, constraints, and tone. Wait for my answers.”
- Save your answers. One commenter wished for a middle path: keep a few quick settings as templates. You can do that today with a note that holds your usual audience and tone, and paste it in as default context. If you write for more than one audience, keep two or three short versions so you can swap them in seconds.
- Give the builder useful feedback. “Nice” doesn’t help. Say which mode you used, what you asked for, and where the output missed. That’s the kind of review they asked for. If you can, mention one specific question you wish it had asked, since that is the easiest thing for a solo builder to act on.
Why this one is worth a look
This is the kind of small build I like seeing: one clear job, one real design tension, and a builder who asked for feedback instead of pretending it was finished. Whether prompt engineering is dead or not (the comments are split), the habit of giving a model good context is not going anywhere.
Try it here: https://ai-prompt-library-blue-seven.vercel.app/
Then tell the builder what you found. A few honest lines from you might be the thing that keeps this project alive. 🚀
Frequently Asked Questions
Q: Is the skip and generate option worth using?
One reader found it a trap: they kept skipping, then regretted the result. Another found that a couple of quick clarifying questions gave noticeably better output than instant generation, because the extra context pointed the model in the right direction. For a throwaway prompt, skipping is fine. If you plan to reuse the prompt, answer at least a few questions. One reader suggested a middle path, saving your common settings as templates so you don’t retype them every time.
Q: Why not just build the prompt inside ChatGPT or Claude?
One commenter argues that a chat assistant can ask the same questions on the fly and tailor them to your task, so a separate tool adds a step without much payoff. That’s a fair point if you already work in an LLM client. The case for a structured picker is repeatability: you get the same well-chosen questions every time, and a library of rated prompts gives you a starting point instead of a blank chat. Try both on the same subject and keep whichever output you actually use.
Q: How many questions should a prompt builder ask?
The detailed mode asks five, which can feel like a lot when you just want a quick draft. The reader who had good results with a few clarifying questions suggests that a small number of sharp questions may beat a long form. When you answer, focus on audience, output format, and constraints, since those are the details a model is most likely to guess wrong.
Kinda gave up but still want to give it one last try!
by u/clavam in PromptEngineering