Best AI Prompts: Five You Can Actually Reuse

Most "best AI prompts" lists are a single clever-sounding line with nothing behind it — no example of what you would actually paste in, no sense of what a good result looks like, and no way to tell whether it worked. That is the wrong unit. A prompt is only reusable if it still produces something useful once you swap in your own input, and if you can tell, afterwards, whether the output is actually right. The five below are built around that, the same way how to write a prompt that works on the first try sets out the general method behind all of them.

What makes a prompt worth keeping

A prompt earns a place in a list like this one if it names the task as a deliverable, states who the result is for, and tells you what to leave out — that is the same guidance OpenAI and Anthropic give in their own vendor documentation, and it is worth trusting precisely because it comes from the people who built the thing. A prompt missing any of those three gets filled in with the model's own default guess, which tends toward generic — longer, hedgier, and less useful than what you would have written yourself.

Five prompts worth reusing

• —Summarising something long for a specific reader. "Here is the full report: [paste]. Summarise it for someone who has two minutes and has not read it. Five bullets, lead with anything that changes what they should do this week. Skip methodology." Naming the reader and the time budget is what stops this coming back as a bland restatement.
• —Turning messy notes into action items with owners. "Here are my raw meeting notes: [paste]. Pull out every decision and action item, with who owns it and by when, if either is stated. Flag anything ambiguous as a question rather than guessing at it." ChatGPT prompts for business has four more templates built the same way for other office tasks.
• —Learning something unfamiliar, with a check built in. "Explain how compound interest actually works, as if I have never taken out a loan. Then quiz me with three questions, and don't give me the answers until I attempt them." Asking to be quizzed afterwards, not just explained to, matters more than it sounds — retrieval practice, being asked a question rather than re-reading an explanation, is the more durable way to learn, and an explanation on its own does not give you that.
• —Tightening your own writing without losing your voice. Paste a paragraph you wrote and ask: "Cut this by a third without losing the main point, and tell me which sentence was doing the least work." You get a sharper draft, and you learn which of your own habits is padding the sentence.
• —Getting a second opinion on a decision you have already framed. "I am choosing between A and B: [your actual constraints]. List the real trade-offs of each, and the one question I have not thought to ask myself yet." This one works because you supplied the actual decision and its limits — the model is organising inputs you already have, not inventing new ones.

A worked example, start to finish

Weak: "Summarise this report."

Better: "Summarise this report for our head of sales, who has not read it and has two minutes. Five bullets, under 100 words total. Lead with anything that changes what her team should do this quarter. Skip methodology."

Same model, same report, twenty extra seconds of typing. The first version returns a competent restatement of the whole document, equally weighted, that the reader still has to mine for the one thing that matters to her. The second comes back already sorted by what she needs to act on. Showing the model a worked example inside the prompt, the way the list above does with its quoted text, carries more information about what you actually want than a paragraph of adjectives would — the same principle behind why one example changes a model's output more than a description of the style does.

The prompt you should not reuse

None of the five above ask the model to recall a fact from memory — every one works from text you pasted in, or constraints you already know, which is deliberate. The prompt to be wary of is the mirror image: "What is [a specific, checkable fact you did not supply]?" asked cold, with nothing attached. A survey of hallucination in large language models documents fluent, confident answers regardless of whether the facts behind them are real, and a confident wrong answer reads exactly like a right one until you check it against something else. What AI is actually bad at covers this gap in full if you want the general version rather than just this one example of it.

What to check before you use any of these

1. 01Read the output against the source text you pasted in, not against how plausible it sounds on its own — plausible and correct are not the same test.
2. 02If a draft comes back generic, check which part of the prompt was missing — the reader, the format, or the actual input — before assuming the model simply cannot do the task.
3. 03For the quiz prompt specifically, attempt an answer before looking at the explanation again. The quiz only works as a check if you actually try it cold.
4. 04If the task needs several rounds of searching and reading rather than one pass, deep research prompts has a template built for that shape instead of this one.

One more habit worth building into the second-opinion prompt specifically: ask the question without previewing the answer you expect. "Here are my two options — which is better?" gets a more independent answer than "I think option A is better, am I right?" — research on how these models respond to a stated expectation has found they tend to agree with the number or the choice you handed them rather than checking it on its own terms. The fix costs nothing: ask first, state your own lean second, if at all.

A reusable prompt beats a one-off clever line precisely because you can run it again next week on a different input and trust the shape of what comes back. That is also why running a vague prompt through a prompt enhancer tool rarely fixes it — dressing up a request in more words does not add the missing reader, format, or input. The same structure holds even when the output is a picture rather than a paragraph: AI image prompts covers the six-part version for that case, and funny ChatGPT prompts shows the identical rule still holding when the goal is a laugh rather than a deliverable.

Coursium teaches exactly this skill in short lessons, including practice tasks where you take a weak prompt of your own and rewrite it against a checklist like the one above. Stay ahead of AI by learning the tools on your phone.