I'd rather read the prompt

It's been obvious since ChatGPT blew up in early 2023 that educators had to rethink how they educate.

I agree that this situation that the author outlines is unsatisfactory but it's mostly the fault of the education system (and by extension the post author). With a class writing exercise like the author describes, of course the students are going to use an LLM, they would be stupid not to if their classmates are using it.

The onus should be on the educators to reframe how they teach and how they test. It's strange how the author can't see this.

Universities and schools must change how they do things with respect to AI, otherwise they are failing the students. I am aware that AI has many potential and actual problems for society but AI, if embraced correctly, also has the potential to transform the educational experience in positive ways.

> Students who know least of all and don't understand the purpose of writing or problem solving or the limitations of LLMs are currently wasting years of their lives

Exactly. I tend to think that the role of a teacher is to get the students to realise what learning is all about and why it matters. The older the students get, the more important it is.

The worst situation is a student finishing university without having had that realisation: they got through all of it with LLMs, and probably didn't learn how to learn or how to think critically. Those who did, on the other hand, didn't need the LLMs in the first place.

> I should hope that the purpose of a class writing exercise is not to create an artifact of text but force the student to think

I'm there for the degree. If I wanted to learn and engage with material, I could save $60,000 and do that online for free, probably more efficiently. The purpose of a class writing exercise is to get the university to give me the degree, which I cannot do by actually learning the material (and which, for classes I care about, I may have already done without those exercises), but can only do by going through the hoops that professors set up and paying the massive tuition cost. If there were a different system where I could just actually learn something (which probably wouldn't be through the inefficient and antiquated university system) and then get a valid certificate of employability for having actually learned it, that would be great. Unfortunately, however, as long as university professors are gatekeepers of the coveted certificate of employability, they're going to keep dealing with this incentive issue.

You're missing trees for forest.

When I was a kid and got an assignement for writing an essey about "why good forces prevailed in Lords of the Rings" as a gate check to see if I actually read the novel I had three choices: (a) read the novel and write the essey myself (b) find an already written essey - not an easy task in pre-internet era but we had books with esseys on most common topics you could "copy-paste" - and risk that the professor is familiar with the source or someone else used the same source (c) ask class mate to give me their essey as a template and rephrase it as my own

A and C would let me learn about the novel and let me polish my writing skills.

Today I can ask ChatGPT to write me a 4 pages essay about a novel I've never heard of and call it a day. There's no value gained in the process.

That's a simple example. The problem is that the same applies to programming. Novice programmer will claim that LLM give them power to take on hard tasks and programm in languages they were not familiar before. But they are not gaining any skill nor knowledege from that experience.

If I ask google maps to plot me a directions from Prague to Brussels it will yield a list of turns that will guide me to my destinations, but by any means I can't claim I've learned topography of Germany in the process.

An analogy I've heard is that it's like using a forklift at the gym. The point is not to get an object from point A to point B, it's to develop skills.

> It's been incredibly blackpilling seeing how many intelligent professionals and academics don't understand this

I figured this out in high school. It can’t be all that uncommon of a thought that if you are already in school and paying and given time to learn, you might as well do so?

I always get triggered when people argue against „rote memorization” - but it also is technique that builds up knowledge, skills and experience.

Even if one won’t need that specific know how after exams - just realization how much one can memorize and trying out some approaches to optimize it is where people grow/learn.

I'm far from the first to make this observation but LLMs are like anti-compression algorithms when used like that, a simple idea gets expanded into a bloated mess by an LLM, then sent to someone else who runs it through another LLM to summarize it back to something approximating the original prompt. Nobody benefits aside from Sam Altman and co, who get to pocket a cool $0.000000001 for enabling this pointless exercise.

Something I’ve found very helpful is when I have a murky idea in my head that would take a long time for me to articulate concisely, and I use an LLM to compress what I’m trying to say. So I type (or even dictate) a stream of consciousness with lots of parentheticals and semi-structured thoughts and ask it to summarize. I find it often does a great job at saying what I want to say, but better.

(See also the famous Pascal quote “This would have been a shorter letter if I had the time”).

P.s. for reference I’ve asked an LLM to compress what I wrote above. Here is the output:

When I have a murky idea that’s hard to articulate, I find it helpful to ramble—typing or dictating a stream of semi-structured thoughts—and then ask an LLM to summarize. It often captures what I mean, but more clearly and effectively.

“Someone sent me this ai generated message. Please give me your best shot at guessing the brief prompt that originated the text”.

Done, now ai is just lossy prettyprinting.

https://marketoonist.com/2023/03/ai-written-ai-read.html

This is how I've felt about using LLMs for things like writing resumes and such. It can't possibly give you more than the prompt since it doesn't know anything more about you than you gave it in the prompt.

It's much more useful for answering questions that are public knowledge since it can pull from external sources to add new info.

Chatgpt very useful for adding softness and politeness to my sentences. Would you like more straight forward text which probably will be rude for regular american?

Recently I wasted half a day trying to make sense of story requirements given to me by a BA that were contradictory and far more elaborate than we had previously discussed. When I finally got ahold of him he confessed that he had run the actual requirements through ChatGPT and "didn't have time to proofread the results". Absolutely infuriating.

This is the CRUX of the issue. Even with SOTA models (Sonnet 3.5, etc) - the more open-ended your prompt - the more banal and generic the response. It's GIGO turtles all the way down.

I pointed this out a few weeks ago with respect to why the current state of LLMs will never make great campaign creators in Dungeons and Dragons.

We as humans don't need to be "constrained" - ask any competent writer to sit quietly and come up with a novel story plot and they can just do it.

https://news.ycombinator.com/item?id=43677863

That being said - they can still make AMAZING soundboards.

And if you still need some proof, crank the temperature up to 1.0 and pose the following prompt to ANY LLM:

  Come up with a self-contained single room of a dungeon that involves an 
  unusual puzzle for use with a DND campaign. Be specific in terms of the 
  puzzle, the solution, layout of the dungeon room, etc. It should be totally 
  different from anything that already exists. Be imaginative. 

100% agree.

LLMs may seem like magic buy they aren't. They operate within the confines of the context they're given. The more abstract the context, the more abstract the results.

I expect to need to give a model at least as much context as a decent intern would require.

Often asking the model "what information could I provide to help you produce better code" and then providing said information leads to vastly improved responses. Claude 3.7 sonnet in Cline is fairly decent at asking for this itself in plan mode.

More and more I find that context engineering is the most important aspect of prompt engineering.

> I'd love to hear any counterpoints from folks who have used LLMs lately to get academic or creative writing done

They’re great at proofreading. They’re also good at writing conclusions and abstracts for articles, which is basically synthesising the results of the article and making it sexy (a task most scientists are hopelessly terrible at). With caveats:

- all the information needs to be in the prompt, or they will hallucinate;

- the result is not good enough to submit without some re-writing, but more than enough to get started and iterate instead of staring at a blank screen.

I want to use them to write methods sections, because that is basically the exact same information repeated in every article, but the actual sentences need to be different each time. But so far I don’t trust them to be accurate with technical details. They’re language models, they have no knowledge or understanding.

For creative and professional writing, I found them useful for grammar and syntax review, or finding words from a fuzzy description.

For the structure, they are barely useful: Writing is about having such a clear understanding, that the meaning remains when reduced to words, so that others may grasp it. The LLM won't help much with that, as you say yourself.

In my experience Gemini can be really good at creative writing, but yes you have to prompt and edit it very carefully (feeding ideas, deleting ideas, setting tone, conciseness, multiple drafts, etc).

https://old.reddit.com/r/singularity/comments/1andqk8/gemini...

I think the author has a fair take on the types of LLM output he has experience with, but may be overgeneralizing his conclusion. As shown by his example, he seems to be narrowly focusing on the use case of giving the AI some small snippet of text and asking it to stretch that into something less information-dense — like the stereotypical "write a response to this email that says X", and sending that output instead of just directly saying X.

I personally tend not to use AI this way. When it comes to writing, that's actually the exact inverse of how I most often use AI, which is to throw a ton of information at it in a large prompt, and/or use a preexisting chat with substantial relevant context, possibly have it perform some relevant searches and/or calculations, and then iterate on that over successive prompts before landing on a version that's close enough to what I want for me to touch up by hand. Of course the end result is clearly shaped by my original thoughts, with the writing being a mix of my own words and a reasonable approximation of what I might have written by hand anyway given more time allocated to the task, and not clearly identifiable as AI-assisted. When working with AI this way, asking to "read the prompt" instead of my final output is obviously a little ridiculous; you might as well also ask to read my browser history, some sort of transcript of my mental stream of consciousness, and whatever notes I might have scribbled down at any point.

> I'd love to hear any counterpoints from folks who have used LLMs lately to get academic or creative writing done

I commented in another thread. We're using image and video diffusion models for creative:

https://www.youtube.com/watch?v=H4NFXGMuwpY

Still not a fan of LLMs.

A LLM can't do that? I'm a little surprised.

(I know jack all about robotics but that sounds like a pretty common assignment, the kind an LLM would regurgitate someone else's homework.)

FWIW: Your original comment, in the first message you sent ChatGPT, was way better than the one you posted. Simple, authentic, to the point

> would you really rather read all the prompts and drafts instead of the final version?

I think you missed the point of the article. They did not mean it literally: it's a way to say that they are interested in what you have to say.

And that is the point that is extremely difficult to make students understand. When a teacher asks a student to write about a historical event, it's not just some kind of ceremony on the way to a degree. The end goal is to make the student improve in a number of skills: gathering information, making sense of it, absorbing it, being critical about what they read, eventually building an opinion about it.

When you say "I use an LLM to dump my thoughts and get help organising them", what you say is that you are not interested in improving your ability to actually absorb information. To me, it says that you are not interested in becoming interesting. I would think that it is a maturity issue: some day you will understand.

And that's what the article says: I am interested in hearing what you have to say about a topic that you care about. I am not interested into anything you can do to pretend that you care or know about it. If you can't organise your thoughts yourself, I don't believe that you have reached a point where you are interesting. Not that you will never get there; it just takes practice. But if you don't practice (and use LLMs instead), my concern is that you will never become interesting. This time is wasted, I don't want to read what your LLM generated from that stuff you didn't care to absorb in the first place.

Exactly it is a tool which needs skill to use. I would add extra use of mine:

- To "Translate to language XYZ", and that is not sometimes strightforward and needs iterating like "Translate to language <LANGUAGE> used by <PERSON ROLE> living in <CITY>" and so on.

And the author is right, I use it as 2nd-language user, thus LLM produces better text than myself. However I am not going to share the prompt as it is useless (foreign language) and too messy (bits of draft text) to the reader. I would compare it to passing a book draft thru editor and translator.

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You get what you measure, and you should expect people to game your metric.

Once upon a time only the brightest (and / or richest) went to college. So a college degree becomes a proxy for clever.

Now since college graduates get the good jobs, the way to give everyone a good job is to give everyone a degree.

And since most people are only interested in the job, not the learning that underpins the degree, well, you get a bunch of students that care only for the pass mark and the certificate at the end.

When people are only there to play the game, then you can't expect them to learn.

However, while 90% will miss the opportunity right there in front of them, 10% will grab it and suck the marrow. If you are in college I recommend you take advantage of the chance to interact with the knowledge on offer. College may be offered to all, but only a lucky few see the gold on offer, and really learn.

That's the thing about the game. It's not just about the final score. There's so much more on offer.

> Most people do view obtaining a degree as just a hurdle to getting a decent job

Then fail to actually learn anything and apply for jobs and try to cheat the interviewers using the same AI that helped them graduate. I fear that LLMs have already fostered the first batch of developers who cannot function without it. I don't even mind that you use an LLM for parts of your job, but you need to be able to function without it. Not all data is allowed to go into an AI prompt, some problems aren't solvable with the LLMs and you're not building your own skills if you rely on generated code/configuration for the simpler issues.

> Most people do view obtaining a degree as just a hurdle to getting a decent job, that’s just the economics of it.

Because those who recruit based on the degree aren't worth more than those who get a degree by using LLMs.

Maybe it will force a big change in the way students are graded. Maybe, after they have handed in their essay, the teacher should just have a discussion about it, to see how much they actually absorbed from the topic.

Or not, and LLMs will just make everything worse. That's more likely IMO.

> I fully support the author’s point

I don't. I think the world is falling into two camps with these tools and models.

> I now circle back to my main point: I have never seen any form of create generative model output (be that image, text, audio, or video) which I would rather see than the original prompt. The resulting output has less substance than the prompt and lacks any human vision in its creation. The whole point of making creative work is to share one’s own experience

Strong disagree with Clayton's conclusion.

We just made this with AI, and I'm pretty sure you don't want to see the raw inputs unless you're a creator:

https://www.youtube.com/watch?v=H4NFXGMuwpY

I think the world will be segregated into two types of AI user:

- Those that use the AI as a complete end-to-end tool

- Those that leverage the AI as tool for their own creativity and workflows, that use it to enhance the work they already do

The latter is absolutely a great use case for AI.

I've heard this called the toupee fallacy. Not all toupees are bad, but you only spot the bad toupees.

The vast majority of the time people question whether or not an image or writing is "AI", they're really just calling it bad and somehow not realizing that you could just call the output bad and have the same effect.

Every day I'm made more aware of how terrible people are at identifying AI-generated output, but also how obsessed with GenAI-vestigating things they don't like or wouldn't buy because they're bad.

Yeah, the author here is as much a part of the problem. If you let students get away with submitting ChatGPT nonsense, of course they’re going to do that - they don’t care about the 3000 words appeal to emotion on your blog, they take the path of least resistance.

If you’re not willing to cross out an entire assignment and return it to the student who handed it in with “ChatGPT nonsense, 0” written in big red letters at the top of it, you should ask yourself what is the point of your assignments in the first place.

But I get it, university has become a pay-to-win-a-degree scheme for students, and professors have become powerless to enforce any standards or discipline in the face of administrators.

So all they can do is give the ChatGPT BS the minimum passing grade and then philosophize about it on their blog (which the students will never read).

Yeah this is what I did the one time I invigilated/marked a Matlab exam. Very obvious cheating (e.g. getting the right answer with incorrect code). But no way was I going through the admin of accusing them of cheating. They just got a 0.

Are you just assuming that a student who you think used an LLM would be unwilling to escalate?

I would have thought that giving 0s to correct solutions would lead to successful complaints/appeals.

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I think the attribution itself is a certain form of cowardice. If one is actually confident that a summary is correct they'd incorporate it directly. Leaving in the "Copilot says" is an implicit attempt to weasel out of taking responsibility for it.

LLMs aren't even that good at summarizing poorly structured text, like email discussions. They can certainly cherry-pick bits and pieces and make a guess at the overall topic, but my experience has been that they're poor at identifying what's most salient. They get particularly confused when the input is internally inconsistent, like when participants on a mailing list disagree about a topic or submit competing proposals.

I often find Copilot summaries to be more or less an attempt at mainsplaining a simple change. If my tiny PR with a one line description requires Copilot to output a paragraph of text about it it’s not a summary, it’s simply time wasted on someone who loves to talk.

I've noticed that even on here, which is generally extremely bullish on LLMs and AI in general, people get instantly downvoted into oblivion for LLM copypasta in comments. Nobody wants to read someone else's slop.

It is an admission of incompetence. If you need a summary, why don't you add it yourself? Moreover, any person nowadays can easily create a chatGPT summary if necessary. It is just like adding a page of google search results to your writing.

I checked your website after this and wasn't disappointed. Funny stuff.

You assume that the teachers job is to catch when someone is cheating; its not. The teachers job is to teach, and if the kids don't learn because their parents allow them to cheat, don't check them at all, and let them behave like shitheads, then the kids will fail in life.

> there is 0 way of telling if the work is real or not, at which point why bother?

I might argue you couldn't really tell if it was "real" before LLMs, either. But also, reviewing work without some accompanying dialogue is probably rarely considered a joy anyway.

On reviewing students' work: people exchange copies, get their hands on past similar assignments, get friends to do their homework , potentially each of them shadow the other in fields they're good at etc.

There always was a bunch of realistic options to not actually do your submitted work, and AI is merely makes it easier, more detectable and more scalable.

I think it moves the needle from 40 to 75, which is not great, but you'd already be holding your nose at student work half of the time before AI, so teaching had to be about more than that (and TBH it was, when I was in school teachers gave no fuck about submitted work if they didn't validate it by some additional face to face or test time)

> a decent cheater will quickly discover ways to conduce their LLM into producing text that is very difficult to detect

Do you have any examples of this? I've never been able to get direct LLM output that didn't feel distinctly LLM-ish.

> there is 0 way of telling if the work is real or not

Talk to the student, maybe?

I have been an interviewer in some startups. I was not asking leetcode questions or anything like that. My method was this: I would pretend that the interviewee is a new colleague and that I am having coffee with them for the first time. I am generally interested in my colleagues: who are they, what do they like, where do they come from? And then more specifically, what do they know that relates to my work? I want to know if that colleague is interested in a topic that I know better, so that I could help them. And I want to know if that colleague is an expert in a topic where they could help me.

I just have a natural discussion. If the candidate says "I love compilers", I find this interesting and ask questions about compilers. If the person is bullshitting me, they won't manage to maintain an interesting discussion about compilers for 15 minutes, will they?

It was a startup, and the "standard" process became some kind of cargo culting of whatever they thought the interviews at TooBigTech were like: leetcode, system design and whatnot. Multiple times, I could obviously tell in advance that even if this person was really good at passing the test, I didn't think it would be a good fit for the position (both for the company and for them). But our stupid interviews got them hired anyway and guess what? It wasn't a good match.

We underestimate how much we can learn by just having a discussion with a person and actually being interested in whatever they have to say. As opposed to asking them to answer standard questions.

Surely this just makes a mockery of the same tone and style that indicates someone put effort and thought into producing something. This just seems in net to waste everyone's time with no benefit to us.

I can't even think of what the new set of expectations would even be of that shift were to occur.