Four sentences separate a garbage AI itinerary from one you’d actually follow. A Redditor over in r/PromptEngineering figured that out after months of typing throwaway one-liners. The original poster, u/FirmCry2135, kept getting the same generic mush no matter what they typed. “Plan a Kyoto trip,” “fix this error,” “draw a cat girl,” it didn’t matter, the results were interchangeable.
Quick Start
Skip the one-liner. Force every prompt into four parts instead. Placeholders for what only you know, a defined output format, a list of constraints, and a note on why each piece is there. That’s the whole system, and it holds up whether you’re planning a trip or debugging code.
Old Way vs New Way
Here’s the contrast. The old way is a single sentence and a shrug: “plan a Kyoto trip.” The AI has no dates, no budget, no idea what format you want. So it invents all three and hands you an essay nobody asked for.
The new way front-loads exactly what the model is missing. You tell it what info comes from you, what shape the answer should take, and what’s off-limits. This contributor’s framing is blunt: if you don’t give the AI dates and budget, it makes them up. And the “do not” list, they say, is where most of the quality comes from.
The Four-Part Fix 🔧
Here’s the structure, broken down:
- 📍 Placeholders for what only you know. Dates, budget, skill level, whatever the model can’t guess. Leave them blank on purpose so you’re forced to fill them in before you hit send.
- 📋 Output format. Say exactly what shape you want back, like “day-by-day list with a cost summary at the end.” Skip this step and you get a wall of text instead of something skimmable.
- 🚫 Constraints. Spell out what’s off-limits: “no rental cars, don’t exceed the budget.” This is the step most people skip, and this Redditor says it’s the one doing the heavy lifting on quality.
- Why each part is there. This one gets skipped the most, and it’s the one that actually matters long-term. If you don’t know why a line is in your prompt, you can’t adapt it for the next one.
The Example, Word for Word
The post includes a real example, and it’s worth reproducing exactly as the creator wrote it. Input: a one-line request for a “4-day trip to Kyoto in November, 3 friends, ~50,000 yen each, by train.” Output prompt, shortened:
Departure city: [ ] / Travel dates: [ ] / Activities we enjoy: [ ]
Create a day-by-day itinerary with morning/afternoon/evening, train routes, and estimated costs so we can track the 50,000 yen budget. Present it as a table with a total at the end.
Do not suggest activities that require a car, and do not exceed the budget.
Notice what’s happening in that block. The placeholders isolate exactly three unknowns. The format line locks in a table with a running total. And the constraint line kills the two most common failure modes: car-based suggestions and budget creep. Nothing is left to the model’s imagination, which is the entire point of the exercise.
Getting tired of typing this out by hand is apparently what pushed the original poster to build a small free tool. Feed it your one-liner, and it hands back the full four-part prompt plus notes on why each piece is there. No sign-up needed for your first try. That makes it an easy way to see the structure applied to your own request before you try writing it from scratch.
One commenter mentioned running a similar setup through a reusable “meta prompt” gem or GPT. That tracks. Once you’ve written this structure a dozen times by hand, turning it into a template you reuse is the obvious next move. That could be a saved custom instruction or a small script of your own.
Try It Yourself
The travel example is just the easiest one to follow, but the structure isn’t specific to trip planning. Swap “travel dates” for “current codebase version” and “budget” for “line count,” and the same four parts clean up a debugging prompt just as well. The pattern is the point, not the itinerary.
If you want to test this on your next prompt, here’s the practical path:
- Take whatever one-liner you’d normally type into ChatGPT or Claude.
- Add your four sections: placeholders, format, constraints, reasoning.
- Run it once, check the output against your constraints, and tighten the “do not” list if anything slipped through.
That last step matters more than people expect. The first draft of your constraints list is never complete. You only find the gaps once you see the AI dodge them, and every dodge becomes a new line in the next version.
Give the four-part structure a shot on your next prompt, even a small one. You’ll notice the difference before you finish reading the output.
Frequently Asked Questions
Q: Isn’t using a meta prompt optimizer or reusable templates faster than writing the 4-part structure each time?
Both work, it depends on your workflow. Meta prompt optimizers or template libraries are great if you ask similar questions repeatedly. But if your needs change (different budgets, activity types, group sizes), the 4-part structure gives you the muscle to adapt on the fly. Some users find templates save time initially, while others prefer understanding the “why” behind each section so they can modify prompts confidently when templates don’t fit.
Q: Why is specifying output format so critical? Can’t AI just figure out what I need?
AI can guess, but it guesses wrong, you’ll get an essay when you need a table, or prose when you need a checklist. Specifying “day-by-day itinerary as a table with costs” cuts the back-and-forth in half. Output format acts as a constraint that forces clarity on both ends.
Q: How do I know what constraints to include in my prompts?
Start with your hard limits (budget, time, tools you don’t have access to). Then add things you want to *avoid* based on what the AI suggested last time that annoyed you. The “no rental cars” in the example came from experience, the author knew AI defaulted to car rentals. Your constraints are learned, not guessed.
Q: Does the 4-part structure work for prompts that aren’t travel planning?
Yes. It works for coding help (placeholder: “I’m using Node 18”; output: “code block with comments”; constraints: “no dependencies”), writing (placeholder: “I have 5 years experience”; output: “short form for Twitter”; constraints: “no jargon”), or debugging. Any prompt benefits from knowing what you know, how you want it formatted, and what to avoid.
I kept asking AI “plan me a trip” and getting garbage. Here’s the structure that fixed it (and a free tool I built around it)
by u/FirmCry2135 in PromptEngineering