Most people ask AI for a word count and then trim the answer by hand. This approach asks for a shape the model can actually hold, and the trimming goes away.
One Reddit user spent two weeks cutting down every ‘exactly 50 words’ blurb before they thought to count. The results were never 50. They landed between 65 and 80 across three different chats. So they ran a quick test instead of complaining, and the results are worth stealing.
The key idea: ask in units the model can count
A language model doesn’t read or write words. It works in tokens, which are chunks of text, and a token is usually a bit less than one word. Short, common words are often one token. Longer or rarer words get split into two or three pieces. There’s no running counter ticking up while it writes, so the model can’t stop at word 50 the way you would if you were typing. ‘Exactly 50 words’ is more of a vibe than a rule to it.
Sentences, bullets, steps and lines are different. The model produces those one chunk at a time, and it knows when a chunk is finished. A bullet starts, a bullet ends, and the next one begins. Those boundaries show up clearly in the text it has already written, so it can see where it is. That makes them a shape it can hold.
Old way vs new way
The old way is to set a number, get an answer that’s close but wrong, and then edit it down yourself. You repeat that every time, on every prompt, and the time adds up. The new way is to describe the container and let the length follow from it.
The test used the same topic, dropshipping, in three formats:
- ‘Explain dropshipping in exactly 50 words.’ Three runs gave 73, 66 and 78 words. Not close.
- ‘Explain dropshipping in under 100 words.’ Anywhere from 71 to 92 words. The ceiling barely mattered, and the spread was wide enough that you still couldn’t plan a layout around it.
- ‘Explain dropshipping in 3 bullets, max 2 sentences each.’ Three runs, three clean hits. Every answer had three bullets, and none ran past two sentences.
The word counts drift every time. The countable structure doesn’t. You can repeat this on any topic in under a minute. Try it on something you already know well, like your own product or a process from your job, so you can judge the quality as well as the length.
How to apply it
- 📏 Swap word counts for structure. Instead of ‘in 50 words’, write ‘in 3 bullets’ or ‘in two sentences’. If you need a rough length for a tweet, a product card or a caption, work out how many sentences usually fit and ask for that number.
- 🔢 Name the size of each piece. ‘Two sentences each’ or ‘five steps’ gives the model a clear stopping point. ‘One line per step’ works well for instructions, and ‘one sentence per point’ works well for summaries.
- 🧪 Keep counts flat. ‘Five examples, one sentence each’ still slips sometimes, with the odd two-sentence example. Nested limits are closer to a coin flip, so if you need a list inside a list, split it into two prompts.
- ✂️ Check the output once. Count what you get on your first few runs. Don’t assume it worked. If one piece keeps running long, tighten that piece, for example by changing ‘a sentence’ to ‘a sentence under ten words’.
The poster says their blurbs stopped needing hand trimming the same week they switched.
The fix wasn’t a smarter prompt. It was a prompt that matched how the model actually writes.
What about plain prose?
This is the open question from the thread. If you need one paragraph with no bullets, what unit do you use? The commenters had a few ideas worth trying:
- Fix the sentence count, then fix the sentence size. Something like ‘one paragraph of exactly four sentences, and keep every sentence short enough to say in one breath’. The breath test is fuzzy, but it gives the model a feel for length that a raw number doesn’t.
- Use two passes. Let the model write freely, then send a second message asking it to rewrite the text to a tighter limit and check its own length. The second pass works better because the full draft is now on the page, and the model can see what to cut.
Neither is a guarantee. Both are better than hoping ’50’ gets heard.
Try it today
Pick a prompt you use every week where you currently set a word limit. Rewrite it with bullets or sentences, run it three times, and see if the length holds. Write down the results next to your old word-count version so you can see the difference for yourself. If you’ve found a better trick for single-paragraph prose, share it. I’d love to steal it!
Frequently Asked Questions
Q: Why won’t the model hit an exact word count?
The model works in tokens, not words, and it has no running counter while it writes. A number like 50 works more as a hint than a target, so answers drift. Asking for a structure it can track as it goes, like three bullets or two sentences each, holds up much better.
Q: What works best when the output has to be one plain paragraph?
Commenters split on this. One approach is to ask for exactly four short sentences, with each one short enough to say in one breath, since models track sentence boundaries better than word totals. Others use two passes: let the model write freely, then send a second message asking it to trim the text to a limit. If you can run code, count the words with a script and only re-prompt when the text is over.
Q: Do phrases like ‘no filler’ or ‘concise’ help?
Some users found that small wording changes like ‘no filler,’ ‘concise,’ or ’50 words max’ sometimes shrink the output. Others said the results were inconsistent, so they treated these phrases as a nudge rather than a fix. They work best on top of a structural limit, not in place of one.
Q: Why do nested counts still drift, like ‘five examples, one sentence each’?
Flat counts hold up better than nested ones. Even with a clear instruction, you may still get an occasional two-sentence example. If the structure matters, check the output and ask for a fix on the one item that broke instead of rerunning the whole thing.
ask it for exactly 50 words, then count what you get. i kept getting 73.
by u/Inevitable-Good219 in PromptEngineering