How to Learn from Other People’s Prompts (Without the Hype)Permalink

If you’ve spent any time on LinkedIn or Twitter, you’ve probably seen people sharing their “game-changing” ChatGPT prompts. Some claim their prompt will “revolutionize your workflow” or “solve all your problems” or “automate away your analyst/intern/marketer/writer/sales team”.

This hype creates a self-perpetuating feeling of FOMO and drums up a lot of smoke and noise that distracts from all the real value that AI can actually provide.

In reality, many people across industries have found and written about ChatGPT prompts that genuinely work for their specific workflows. These aren’t magic formulas, but they are proven approaches that you can learn from and adapt. The key is knowing where to look, how to evaluate what you find, and most importantly, how to make these prompts work for your specific use case.

Don’t believe the overhyped “this ONE prompt will solve ALL your problems” claims. But don’t dismiss the entire practice either. Learning from other people’s prompts is one of the fastest ways to build your own prompt intuition and see different approaches to structuring prompts effectively.

Where to find promptsPermalink

LinkedIn/Twitter: Industry professionals sharing their workflows (be skeptical of “one prompt solves everything” claims).

GitHub repos: Search for “prompt engineering” or “chatgpt prompts” - many developers share their collections

Reddit communities: r/ChatGPT, r/promptengineering, and other subreddits, where real users share what works.

Prompt libraries: Sites like Awesome ChatGPT Prompts or PromptBase.

How to evaluate prompts you findPermalink

• Look for specificity: Does it clearly define the task and context?
• Check for examples: Good prompts often include example inputs/outputs.
• Assess adaptability: Can you modify it for your use case?
• Test it yourself: Don’t trust claims. Try it with your actual data.

How to adapt promptsPermalink

• Replace generic placeholders with your specific context. Most shared prompts use placeholders like “[your industry]” or “[your use case]”. Fill these in with your actual details.
• Add your own domain knowledge and constraints. The original prompt might work for someone else’s situation, but yours likely has unique requirements, compliance needs, or constraints.
• Use your definition of what “good” looks like. You know better what works for you, and what a “good result” (e.g., good writing) looks like for you might not look like someone else’s.
• Test with your actual data. Don’t just test with fake data. Use real examples from your work to see if the prompt actually delivers value.
• Iterate based on results. Your first adaptation probably won’t be perfect. Refine it based on what the AI outputs, just like you would with any prompt you write from scratch.

The real value: Building your intuitionPermalink

The goal isn’t to find the “perfect prompt” that you can use forever. The goal is to learn different approaches to structuring prompts, see what works in different contexts, and build your own intuition for what makes a prompt effective.

When you try someone else’s prompt, pay attention to:

• How they structure the request (do they use numbered steps? Do they specify format upfront?).
• What context they include (and what they leave out).
• How they handle edge cases (do they anticipate problems? Do they add things like “do NOT include XYZ” or “make sure to avoid ABC”?).
• What makes it work (or not work) for your specific use case.

This observation and adaptation process is what builds real prompt engineering skills, not just copying and pasting.

ConclusionPermalink

Learning from other people’s prompts is a smart strategy, but it’s not a shortcut. You still need to understand why prompts work, adapt them to your context, and iterate based on results. The prompts you find online are starting points, not destinations.

The best way to do it? Treat shared prompts like a simple recipe from a cookbook. You can’t just copy and paste a recipe word for word. You need to figure out what works for you, your preferences (salty? sweet?), what you have in your kitchen, how good you are as a cook, and the like. A good prompt that someone else has used is like a recipe, and it works for them in a certain way but it might not work for you the same. You have to adapt the prompt to your data, your constraints, your understanding of what a “good result” looks like, and for your workflow.

Find a few propts that seem relevant to what you’re working on. Two very quick ways to start are:

• Ask Google “find me prompts related to [insert concept]”.
• Look up prompts on a platform like PromptBase.

Take a few prompts, copy-and-paste them yourself into ChatGPT, take notes on what you like and don’t like about how it did, and tweak them to have something that works for you. Over time, this’ll give you a library of prompts that others started but you refined for your use case. Importantly, it also develops your own intuition for how to write and edit prompts to match what works for you.