I keep running into the same quiet problem when I talk to founders. They open ChatGPT, type one lazy sentence, and then wonder why the answer feels flat. I did the exact same thing for way too long, so no judgment here.
Then I came across a breakdown from an AI professional who builds with these tools every single day, and it reframed how I think about prompting completely. This creator noticed something simple: the gap between a team that flies with AI and one that fumbles almost always comes down to one skill nobody really teaches. Prompting. Not the flashy kind. The practical kind.
The best part? The author pulled together 30 of the sharpest prompting hacks they’ve collected, tested across every major model. I want to walk you through the ones the original poster says changed their own workflow the most.
The reframe that makes everything click
Here’s the mindset shift the expert leads with, and it stuck with me. A prompt isn’t a question. It’s a spec.
The clearer the spec, the less you’re babysitting the output.
Think about how you’d brief a sharp new hire. You wouldn’t mumble one vague line and hope for the best. You’d give them the role, the goal, the format, and the traps to avoid. That’s exactly what the creator says a strong prompt does. Once I started treating my prompts like a written brief instead of a quick text message, the quality of what I got back jumped fast.
7 prompting hacks worth stealing today
The author shared a handful of favorites from the full list of 30. Here are the ones with the biggest payoff, with a quick note on why each one works.
- Persona + Goal + Anti-Goal. Give the model a role, a clear target, and the failure mode to avoid. The clever part is naming the trap out loud. When you tell the AI what a bad answer looks like, it steers away from that outcome before it ever happens.
- Negative constraints. Telling the AI what to skip works faster than only listing what to include. Ask it to avoid jargon, filler, and em dashes, and the writing tightens up immediately. Sometimes the fastest edit is a clear “don’t.”
- Ask the AI to write the prompt. This one feels almost like cheating. Say “write the optimal prompt for this goal” and let the model build its own instructions first. You get a stronger starting point than most of us would write by hand, then you run that.
- Tree of thought. For hard problems, push the model to explore a few different paths before it commits to one. Instead of grabbing the first idea, it weighs a few and picks the strongest. Slower, but far better on tricky reasoning.
- Show, don’t tell for formatting. A small example beats a paragraph of formatting rules every time. Paste one sample of the exact layout you want, and the model matches it. Way cleaner than describing spacing and structure in words.
- Context stacking over perfect prompts. The creator points out that ChatGPT and Claude both perform better with structure and reasoning steps than with one perfectly polished sentence. Stop chasing the magic one-liner. Stack the context instead.
- Human check. Always verify before the output touches a real decision. The original poster admits they learned this one the hard way, and honestly, so has everyone I know. The AI drafts. You still own the call.
Why this matters more than it looks
The line from the post that I keep coming back to is this one. The founders who get this early aren’t smarter. They just stopped guessing and started specifying.
That’s the whole thing. Better prompting isn’t about memorizing tricks. It’s about being specific enough that the model doesn’t have to guess what you meant. And as the expert notes, at bigger scale this is the single lever most people leave on the table.
How to put this into practice this week
You don’t need all 30 hacks on day one. Here’s a simple way to start, based on the creator’s approach:
- Pick one task you already do often, like drafting emails or summarizing notes.
- Rewrite your usual prompt as a spec: role, goal, anti-goal, and a format example.
- Add one or two negative constraints so the model knows what to avoid.
- Run it, then do the human check before you use anything.
Do that a few times and you’ll feel the difference. Less back and forth, less babysitting, more usable output on the first try.
If someone on your team is still prompting like it’s 2023, this is the breakdown to pass along. I thought it was one of the clearest explanations of the spec mindset I’ve seen in a while. Check out the full LinkedIn post for the complete set of 30 hacks and the details behind each one.