How to Write Shorter AI Prompts That Get the Same Answer
Most prompts are about a third longer than they need to be, and the extra third is not doing anything. Not making the answer better, not making it more reliable — just being billed. Here is how to find it without damaging the parts that matter.
First, why length matters at all
For a one-off question in a chat window, it does not. Type however you like.
It starts to matter when a prompt is reused. A system prompt in a production application is sent as input on every request. Ten thousand requests a day means that prompt is billed ten thousand times a day. At that scale the difference between 2,000 tokens and 1,200 tokens is real money — several hundred dollars a month on an otherwise identical product. The mechanics of that are covered in our guide to how LLM API pricing works.
There is a secondary benefit that people notice less: shorter prompts are often more reliable. A model given three hundred words of preamble has more opportunity to latch onto the wrong emphasis than one given a crisp instruction.
What carries instruction and what does not
The whole skill is one question, applied word by word: if I deleted this, would the model be asked to do something different?
If no, it is padding. If yes, keep it, however wordy it looks.
Safe to cut: politeness
Please, kindly, I would like you to, could you, if you don't mind, thank you in advance. These are social lubricant for humans. They convey no task information. "Please could you kindly summarise this document for me" and "Summarise this document" ask for exactly the same thing.
Safe to cut: intensifiers
Very, really, quite, extremely, truly, actually, basically, literally, simply, just. "Write a very detailed summary" and "Write a detailed summary" produce indistinguishable results. The intensifier has no threshold behind it.
Safe to cut: throat-clearing
It is important to note that. I want you to understand that. Keep in mind that. It should be mentioned that. These announce that something is about to be said. Delete them and say the thing.
Safe to compress: wordy connectives
These have shorter exact equivalents:
- in order to → to
- due to the fact that → because
- the fact that → that
- at this point in time → now
- in the event that → if
- a large number of → many
- has the ability to → can
Safe to cut: role-play preamble that does nothing
"You are a highly skilled, world-class expert assistant with decades of experience" is mostly decoration. A short role instruction can genuinely shift register — "You are a technical editor" is useful because it implies a style. The superlatives stacked on top of it are not.
What you must not cut
This is the more important half, and it is where over-enthusiastic trimming does damage.
Keep: examples
Few-shot examples are the highest-value tokens in most prompts. One good example of the input-output pattern you want is usually worth more than several paragraphs describing it. They look expensive because they are long. They are earning their place.
Keep: output format specifications
If you need JSON with particular keys, say so explicitly, and show the shape. Vagueness here costs far more in retries and parsing failures than the tokens ever cost you.
Keep: constraints and boundaries
Word limits, tone requirements, things to avoid, what to do when the input is unexpected. Each of these changes the output. They are instruction, not padding.
Keep: genuine context
Who the audience is, what the document is for, domain facts the model has no way to know. If it is information rather than decoration, it stays.
Keep: edge-case handling
"If the text contains no date, return null rather than guessing." Long, specific, and the difference between a system that works and one that quietly invents data.
A worked example
An original prompt, 94 words:
Hello! I would really like you to please help me out with something. I have a document that I need you to basically summarise for me. It is very important that you kindly make sure the summary is quite short, and I would also really appreciate it if you could actually make sure to include all of the key points. Please note that it is important to note that the summary should be written in a professional tone of voice. Thank you so much in advance for your help with this task!
Trimmed to 34 words:
Summarise the document below in under 150 words. Include every key point. Use a professional tone. If the document is shorter than 150 words, return it unchanged rather than padding.
Roughly a 64% reduction, and the second version is better. "Quite short" became a number the model can act on. An edge case got handled. Everything removed was social padding.
Note what happened there: the shortened version is not simply the original with words deleted. Two vague requirements became specific ones. That is the part automation cannot do for you.
Structure beats prose
For anything complex, a list is shorter and clearer than a paragraph:
Extract from the email below:
- sender name
- requested action
- deadline (ISO date, or null if absent)
Return JSON only.
Try writing that as flowing prose and it takes three times the tokens and is easier to misread.
Automating the mechanical pass
Filler removal is repetitive and rule-shaped, which makes it a good fit for a tool. Our Prompt Optimizer strips the filler words and wordy connectives listed above, collapses redundant whitespace, and shows you the before-and-after token count with the cost difference per call. It runs entirely in your browser.
What it deliberately does not do is rewrite meaning. It will not turn "quite short" into "under 150 words", because guessing your intent is how a tool breaks a prompt. Treat the automated pass as step one, then apply judgement: make vague requirements specific, and check that nothing load-bearing was removed.
When to leave a prompt long
Do not optimise for brevity as an end in itself. A 3,000-token prompt packed with examples that produces a correct result first time is cheaper in practice than a 200-token prompt that needs three attempts — three calls, three lots of output tokens, plus your time.
Length is worth paying for when it buys reliability. It is worth cutting when it buys nothing. Filler always buys nothing, which is why it is the right place to start.
Checklist
- Remove politeness words — they carry no instruction.
- Remove intensifiers and throat-clearing.
- Compress wordy connectives to their one-word equivalents.
- Turn vague adjectives into numbers.
- Keep every example, format spec, constraint and edge case.
- Prefer lists to paragraphs for multi-part instructions.
- Measure the token difference rather than guessing at it.
Try it yourself
Our free prompt optimizer needs no account and adds no watermark — use it right now, as many times as you like.
Open Prompt Optimizer →Frequently Asked Questions
Does being polite to an AI model improve the answer?
Not meaningfully for the reason people assume. Words like please and kindly carry no task information, and removing them does not degrade output quality. What does help is clear structure and specific constraints. If you prefer to be polite out of habit that is entirely fine — just be aware it is costing tokens on every call in a production system.
Will making my prompt shorter make the answer worse?
Only if you cut the wrong things. Removing filler, hedging and redundant framing is safe. Removing examples, output format specifications, constraints or genuine context is not, and usually makes results noticeably worse. The distinction is whether the words change what the model is being asked to do.
How much money does shortening a prompt actually save?
On a single call, a fraction of a cent — not worth your time. It matters when a prompt is reused at scale. A system prompt sent on every request in a production application is billed every single time, so trimming it from 2,000 to 1,200 tokens can save hundreds of dollars a month at high volume.
When should a prompt be long?
When length is doing real work. Few-shot examples, detailed output schemas, domain context the model cannot infer, and explicit edge-case handling all earn their tokens. A 3,000-token prompt full of examples that produces a reliable result is better value than a 200-token prompt that needs three retries.