Do Not Write with an LLM

A Growing Trend

I have been see­ing an in­creas­ingly preva­lent trend of peo­ple show­ing up in on­line spaces proudly flaunt­ing that they are writ­ing with the as­sis­tance of AI. I of­ten see them clar­ify that they are go­ing fur­ther than let­ting the model per­form gram­mar or fact-check­ing. They are de­scrib­ing an idea to an LLM, and giv­ing it com­plete con­trol over the fi­nal writ­ten doc­u­ment. They seem to be proud of this. They should­n’t be.

What Does Not Work

Writing seems to be a shadow of a more fun­da­men­tal and use­ful process we like to call “thinking”. There is a rea­son why LLMs, which are the clos­est thing we cur­rently have to “software that thinks” is trained to think through writ­ing. It seems log­i­cal, then, that we can use this “software that writes” to help along our own think­ing process, right? Wrong.

Writing is one of the most cere­bral ac­tiv­i­ties that hu­mans do on a reg­u­lar ba­sis. I try to sit down and write about a com­plex topic at least once a week. There are a num­ber of rea­sons I do that per­tain­ing to my job in par­tic­u­lar, but there are more gen­er­ally ap­plic­a­ble rea­sons that can ap­ply to any pro­fes­sion.

When a math­e­mati­cian needs to think crit­i­cally about some the­o­rem or proof, they of­ten turn to a dry-erase board or a chalk­board. The math­e­mati­cian then writes in clear no­ta­tion what they imag­ine the prob­lem to look like in their heads. From there, they can ma­nip­u­late that no­ta­tion un­til they reach their de­sired out­come.

The English lan­guage is ex­tremely sim­i­lar, with a key dif­fer­ence. Math­e­mat­i­cal no­ta­tion is specif­i­cally de­signed for a nar­row do­main. Nat­ural lan­guages, on the other hand, can be ap­plied to any field. By writ­ing your idea down on pa­per (or in a Google Doc, it does not mat­ter), you give it sub­stance. This sub­stance can then be shaped it­er­a­tively to­wards a goal of some kind.

No great ideas were writ­ten down on the first try. They started as a vague no­tion, and through writ­ing and rewrit­ing, they be­came some­thing con­crete and valu­able. You can­not dic­tate a vague no­ta­tion to an LLM and ex­pect it to spit out some­thing tan­gi­bly valu­able. Any at­tempt to do so is lazy, and it will re­sult in fail­ure. I have seen it in the wild, and frankly, it is a lit­tle em­bar­rass­ing.

This ar­ti­cle feels es­pe­cially rel­e­vant to­day.

What Does Work

Using an LLM for re­search can be very ef­fec­tive. Searches to ChatGPT or Perplexity can sur­face in­for­ma­tion faster than dig­ging around in a book you read three months ago. For lots of searches, these two sources have com­pletely re­placed Google and DuckDuckGo for me.

They’re also quite ex­cel­lent fact-check­ers. I’ve seen that plenty of folks have re­ceived quite good re­sults by past­ing their work into an LLM with an in­ter­net tool (like ChatGPT with Thinking en­abled).

Finally, I’ve per­son­ally found that LLMs are quite good at find­ing places where I sim­ply for­got to in­sert con­text. Some­times I for­get to de­fine a term or in­sert a rel­e­vant link. Even small mod­els like gemma3n:e2b can catch me.

What You Should Be Doing

When you need to com­mu­ni­cate an idea in your head, sit down with a notepad and a key­board and ex­plain it. I per­son­ally think you should use a gram­mar checker, since they are far less likely to im­pede your thought process, but if you need to, you can in­struct ChatGPT to cri­tique your work once it is fin­ished.

If an idea de­serves an au­di­ence, you’re ca­pa­ble of writ­ing it out your­self. In the same vein, if you can vaguely de­scribe it to ChatGPT, you can vaguely de­scribe it to your in­tended au­di­ence. Do your­self a fa­vor and skip the mid­dle­man.

What We Should Be Building

Now, I’d like to en­ter the sec­tion where I talk specif­i­cally about what this all means for peo­ple who cre­ate tools for writ­ers. That in­cludes both Harper and WordPress.

A tool that helps some­one write is a tool that di­rectly in­ter­faces with how that per­son thinks. That might in­clude what they think about, but more con­cretely I am re­fer­ring to the ac­tual day-to-day process of think­ing through com­plex ideas. If you don’t be­lieve me, it’s prob­a­bly be­cause you skipped ahead in­stead of read­ing this post from the be­gin­ning.

Most cru­cially, we can­not al­low our­selves to build tools that dis­suade peo­ple from ex­press­ing their ideas in the way they imag­ine. This can take the form of fric­tion, like build­ing tools that are so cum­ber­some that one dreads to open them at the start of the work­day. This can also take the form of a tool that crushes or over­writes an au­thor’s core mes­sage.

A com­mon com­plaint I hear about Grammarly (from peo­ple who have switched to Harper from Grammarly) is that it’s too de­mand­ing. When they write prose in a cre­ative or un­usual fash­ion, their ser­vice of­ten forces them back into a colder and more for­mal way of speak­ing. We don’t want to be like them.

For Harper, that means we need to be di­verse in our train­ing datasets and mind­ful of feed­back from our users. For WordPress, it means we need to en­cour­age our LLM in­te­gra­tions to tread lightly and re­spect­fully. They need to ac­cept when they’re wrong. Just as any good hu­man should.

Months af­ter writ­ing this post, I came across an­other. Au­thor Cynthia Dunlop gives a great overview of how de­vel­op­ers re­act to blog posts writ­ten by AI. I highly sug­gest you take a look if you have more time.