Picture it: Tuesday afternoon, deadline looming. You paste a problem into ChatGPT and get four paragraphs of generic advice that could have come from a Wikipedia summary.
The model isn’t the problem. The prompt is.
Someone on r/PromptEngineering figured this out after months of actual testing. The post’s author, u/Electrical-Carpet204, shared three prompts from a refined ChatGPT workflow, and the reaction was immediate. Four different commenters asked for the full list of 50. That kind of response tells you this isn’t just another list post.
Here’s what the Redditor shared, why each prompt works, and how to chain them together.
🧠 Why structured prompts change the output
Most people treat AI like a search engine. Type a question, scan the output, close the tab.
Structured prompts are different. They assign the model a role, give it a specific output format, and constrain the response in useful ways. That combination changes what you get back and how fast you can act on it.
Think about what “generic” actually means. When you give the model no role and no format, it defaults to the statistical middle of everything it was trained on. Structured prompts pull it out of that default lane and into a specific mode of thinking. The model isn’t smarter, but it’s pointed at the right target.
The three prompts in this post cover a complete planning loop: break a problem into options, turn the best option into an action plan, then stress-test that plan for weaknesses. What used to take an afternoon of thinking becomes a 15-minute session.
The reason this Redditor has 50 of these is simple. Reusable templates with clear structure keep producing consistent results. One-off prompts don’t.
⚙️ The three prompts (use these word for word)
Here are the three the author shared, reproduced exactly:
Prompt 1: Break down the problem
“Act as a senior strategist and break this into 3 solutions: [problem]”
Why it works: Role assignment (“senior strategist”) shifts the model’s framing before it generates a single word. Asking for exactly 3 solutions prevents the model from giving you a sprawling list you can’t act on. The bracket keeps it a reusable template you can drop any problem into. Try it with something real: “we’re losing customers in month two” or “our content pipeline keeps stalling.” The specificity of your problem inside the bracket is what separates a useful answer from a generic one.
Prompt 2: Build the action plan
“Turn this into a step-by-step execution plan: [goal]”
Why it works: The phrase “execution plan” tells the model to stop analyzing and start prescribing. You get concrete steps instead of abstract advice. Take one of the three solutions from Prompt 1 and paste it into the bracket here. If the steps feel too high-level, add a follow-up: “Make each step more specific with an owner and a deadline.” That one line forces the model to go from strategy to actual task management.
Prompt 3: Find the holes
“Identify risks and blind spots in this plan: [plan]”
Why it works: This is the step most people skip entirely. Asking specifically for “blind spots” pushes the model past obvious risks into the assumptions you didn’t know you were making. Drop the output from Prompt 2 into this one before you act on anything. If you want to go deeper, follow up with: “What would have to be true for this plan to fail?” That question surfaces the hidden bets baked into your approach.
Run all three in sequence and you’ve covered the full planning loop: options, execution, validation.
💡 Tips and tricks
A few things that make these work better:
- Always chain them. The output of each prompt feeds the next. Don’t use Prompt 3 in isolation or you lose the context the earlier steps built. The chain is the whole point.
- Swap the role in Prompt 1. “Senior strategist” is a solid default, but try “startup founder,” “CFO,” “product manager,” or any role that fits your context. The perspective shifts noticeably. A CFO framing will surface budget constraints. A product manager framing will surface user friction. Same problem, genuinely different outputs.
- Load the brackets with real detail. Vague input gets vague output. Add numbers, constraints, timeline, and stakeholders. “We need more leads” is weak. “We need 30% more qualified leads in 90 days with a team of two and no ad budget” gives the model something to actually work with.
- Use them across tools. These templates run just as well in Claude, Gemini, and GPT-4o. The structure matters more than which model you’re using.
- Save them somewhere. Keep a running doc of prompts that actually work for your workflow. A simple Notion page or even a text file works fine. The author built up 50 of these over months, but you don’t have to start from scratch. Start with these three, use them this week, and add the ones that keep delivering.
The community is already asking for the full list. If the first three are this clean, the rest are worth tracking down.
💬 Join the conversation
Head over to the original thread on r/PromptEngineering to see if the author dropped all 50, share the prompts that work in your own workflow, or just lurk the replies for extra ideas. The thread is picking up!
Frequently Asked Questions
Q: The post says 10 prompts but I only see 3 , what’s going on?
Good catch. The post highlights the three prompts that made the biggest impact on the author’s workflow, not the complete set. They mention having 50 total, but this post focuses on the most effective ones. If you want the full collection, you’d need to reach out to the author directly.
Q: Should I use these word-for-word or tweak them for my situation?
Either way works. Some people keep them consistent across projects for reliability. Others customize them based on their role or context , like swapping “strategist” for “product manager.” The structure is flexible enough to adapt.
Q: Can I use these with other AI tools like Claude or Gemini?
Absolutely. These prompts aren’t ChatGPT-specific , they work with any modern LLM. You might see slight variations in output quality, but the core strategy of role-assignment plus structured requests translates across tools.
Q: Why do these prompts work so well?
Notice the pattern: each one assigns a perspective (strategist, executor, risk assessor), provides context, and asks for something specific. This combination gives the AI clear direction, which leads to sharper, more useful responses than vague questions.
I spent months refining my ChatGPT workflow — here are 10 prompts I actually use
by u/Electrical-Carpet204 in PromptEngineering