Written and reviewed by Manish Kulkarni · About the author
A prompt is the input you give an AI model: your question, any files or pasted text, and sometimes images. The part beginners miss is that the model has no access to anything else. It does not know who you are, what you tried yesterday, or what you were thinking when you typed. If it isn't in the prompt (or in a setting or memory feature you've switched on), it doesn't exist for the model.
That is why two people can ask for "the same thing" and get very different answers. They are not really sending the same prompt.
You do not need a formula, but when a prompt disappoints me it is almost always missing one of these:
Take a vague request: "Help me plan a trip." The model is missing your destination, length, budget, interests and travel style. Depending on the tool, it will either guess and hand back a generic itinerary, or stop and ask you a list of questions first. Either way, you have not got a useful plan yet.
Now add what you know: "Help me plan a 3-day trip for two adults who like hiking but not crowds. Budget is moderate, we'll have a car, and we'd rather have one good day hike than five short walks. Give me a day-by-day plan and tell me what to book in advance."
Nothing technical was added. The second version simply stops the model guessing. Notice also the last sentence, which asks for a specific kind of output. Asking for what you plan to do with the answer is one of the most useful habits you can build.
Language models generate text one piece at a time, choosing among likely continuations, so there is some built-in variation. Ask the same question twice and you may get different wording, a different structure, or occasionally a different conclusion. That is normal behaviour, not a fault. It is also a reason to treat a single answer as a draft, especially for facts, numbers or anything you plan to act on.
Your first prompt rarely needs to be perfect. A better habit is to look at the first answer, say what is wrong with it, and ask again: "Too formal, make it warmer", "You assumed I have a car, I don't", "Cut this to half the length". Each follow-up adds context the model could not have known. Many people give up after one weak answer when two short corrections would have fixed it.
In October 2026 I ran this comparison myself in ChatGPT. First I typed a vague prompt, typo included: "Help me to plant trip."
ChatGPT did not guess. It said it could plan the whole trip from Pune, then asked me six things: when I wanted to travel, for how many days, how many people, whether I was starting from Pune, my approximate budget, and what kind of trip I preferred. It had assumed Pune as the starting point before I said anything, so that part was a guess on its side.
Then I answered in one rushed line: "Next month 8 days 4 /start pune/40000/jungle safari." The reply was a completely different kind of answer. It picked Tadoba as the destination, proposed specific dates in November, laid out an eight-day table, listed a budget that added up to about ₹36,000-40,000, and warned that the safari should be booked before the hotels and trains. It also suggested sleeper class instead of 3AC to keep within my budget.
What I would still check myself. The plan quoted safari prices, train fares and park timings. Those change, and they come from the official booking sites, so before paying for anything I would confirm them there. I also noticed that two sources I looked at disagreed on the weekday price of a buffer-zone safari, which is exactly why I would not trust one number from a chatbot or a blog.
My verdict. The first answer only gave me a basic idea of the trip. The second turned that idea into a plan with dates, costs and a day-by-day schedule that I could actually work from.
No — plain, clear English works well. Being specific matters far more than using any particular jargon or formula.
Often because the question was ambiguous in a way you didn't notice — a phrase with two possible meanings, or missing context the model needed to interpret it correctly.
Not one universal formula — but including context, a clear goal, and any format preferences consistently improves results across most situations.