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Notes -
On Using LLMs Without Succumbing To Obvious Failure Modes
As an early adopter, I'd consider myself rather familiar with the utility and pitfalls of AI. They are, currently, tools, and have to be wielded with care. Increasingly intelligent and autonomous tools, of course, with their creators doing their best to idiot proof them, but it's still entirely possible to use them wrong, or at least in a counterproductive manner.
(Kids these days don't know how good they have it. Ever try and get something useful out of a base model like GPT-3?)
I've been using LLMs to review my writing for a long time, and I've noticed a consistent problem: most are excessively flattering. You have to mentally adjust their feedback downward unless you're just looking for an ego boost. This sycophancy is particularly severe in GPT models and Gemini 2.5 Pro, while Claude is less effusive (and less verbose) and Kimi K2 seems least prone to this issue.
I've developed a few workarounds:
What works:
(Note that you must be proactive. LLMs are biased towards assuming that anything you dump into them as input was written by you. I can't fault them for that assumption, because that's almost always true.)
What doesn't work: I've seen people recommend telling the LLM that the material is from an author you dislike and asking for "objective" reasons why it's bad. This backfires spectacularly. The LLM swings to the opposite extreme, manufacturing weak objections and making mountains out of molehills. The critiques often aren't even 'objective' despite the prompt.*
While this harsh feedback is painful to read, when I encounter it, it's actually encouraging. When even an LLM playing the role of a hater can only find weak reasons to criticize your work, that suggests quality. It's grasping at straws, which is a positive signal. This aligns with my experience, I typically receive strong positive feedback from human readers, and the AI's manufactured objections mostly don't match real issues I've encountered.
(I actually am a pretty good writer. Certainly not the best, but I hold my own. I'm not going to project false humility here.)
A related application: I enjoy
pointless argumentsproductive debates with strangers online (often without clear resolution). I've found it useful to feed entire comment chains to Gemini 2.5 Pro or Claude, asking them to declare a winner and identify who's arguing in good faith. I'm careful to obscure which participant I am to prevent sycophancy from skewing the analysis. This approach works well.Advanced Mode:
Ask the LLM to pretend to be someone with a reputation for being sharp, analytical and with discerning taste. Gwern and Scott are excellent, and even their digital shades/simulacra usually have something useful to say. Personas carry domain priors (“Gwern is meticulous about citing sources”) which constrain hallucination better than “be harsh.”
It might be worth noting that some topics or ideas will get pushback from LLMs regardless of your best effort. The values they train on are rather liberal, with the sole exception of Grok, which is best described as "what drug was Elon on today?". Examples include things most topics that reliably start Culture War flame wars.
On a somewhat related note, I am deeply skeptical of claims that LLMs are increasing the rates of psychosis in the general population.
(That isn't the same as making people overly self-confident, smug, or delusional. I'm talking actively crazy, "the chatbot helped me find God" and so on.)
Sources vary, and populations are highly heterogeneous, but brand new cases of psychosis happen at a rate of about 50/100k people or 20-30 /100k person-hours. In other words:
About 1/3800 to 1/5000 people develop new onset psychosis each year. And about 1 in 250 people have ongoing psychosis at any point in time.
I feel quite happy calling that a high base rate. As the first link alludes, episodes of psychosis may be detected by statements along the lines of:
If a patient of mine were to say such a thing, I think it would be rather unfair of me to pin the blame for their condition on chimpanzees, the practise of organ transplants, Big Aquarium, American intelligence agencies, or Maxar.
(While the CIA certainly didn't help my case with the whole MK ULTRA thing, that's sixty years back. I don't think local zoos or pet shops are implicated.)
Other reasons for doubt:
Case reports ≠ incidence. The handful of papers describing “ChatGPT-induced psychosis” are case studies and at risk of ecological fallacies.
People already at ultra-high risk for psychosis are over-represented among heavy chatbot users (loneliness, sleep disruption, etc.). Establishing causality would require a cohort design that controls for prior clinical risk, none exist yet.
*My semi-informed speculation regarding the root of this behavior - Models have far more RLHF pressure to avoid unwarranted negativity than to avoid unwarranted positivity.
As it happens, I have also been dipping into LLMs-as-beta-readers lately, even going so far as to build an application that can read an entire series of books and learn its "lore," and a custom GPT instance that will "compress" a book into a format optimized to provide context to itself or another GPT. (As you probably know, even the most powerful LLMs do not have a context window large enough to store an entire large novel in memory, let alone a series, and you can't directly upload embeddings to GPT or Claude.) The intent of these projects is so that I can, say, ask GPT to evaluate the fifth book in a series with knowledge of the previous four books. It's a work in progress.
So, some observations. First, sorry dude, but I have major side-eye for your ability to evaluate literary quality. :p
That being said, I have also noticed the tendency of LLMs to glaze you no matter how hard you try to solicit "honest" feedback, unless you resort to tricks like you mentioned. (Telling an LLM the manuscript is by an author you hate and you want it to roast it will work, but that's not exactly useful feedback.)
The hallucination problem is hard to overcome, even with tricks like my token-optimizing scheme. I find that in most sessions, it will stay on course for a while, but inevitably it starts making up characters and events and dialog that weren't in the text.
As long as you can keep it on track, I have found that some of the GPT and Anthropic models are... not terrible as beta readers. They point out some real flaws and in a very generic sense have an "understanding" of pacing and tone and where a scene is missing something. However, the advice tends to be very generic. "You need to show the consequences," "The scene ends too quickly, you should build more tension," "There should be some emotional stakes the reader can connect with," etc. Clearly they have many writing advice books in their training data. There is nothing like true understanding of context or story, just generic pieces it can pattern-match to the writing sample you give it.
And when it comes to specific suggestions, I have yet to see an LLM that is actually a good (not "mediocre and banal but capable of producing literate prose") writer. Its suggestions will be a pastiche of flat TV script dialog and trope-filled scenes.
(That said, any writer will tell you to listen to critics when they point out problems, but don't listen to them when they propose solutions. So in that respect an LLM isn't much different than a human.)
But these are still early days for AI, so I don't doubt that in a few years, we'll have LLMs that can be at least as useful as your average writing workshop. AI writing is already flooding some genres, and while it's usually as easy to spot as AI art is, just as with AI art, a lot of people clearly don't care.
I find it fascinating and I enjoy playing around with it, but yeah, I think AI-generated novels will crowd out human writers in low-brow undiscerning stuff like romance and progression fantasies, and writing those stories will become something people only do as a hobby, just like people are still passionate about chess and go even though no human can beat a computer anymore. I still think we'll need true AGI to write an actual good novel. When you show me an AI that can write a coherent series, with multi-volume character arcs, plot seeds planted in early books that clearly pay off in later ones, literary allusions and metaphors that aren't just clumsy pulled-off-the-shelf ones but deeply enmeshed in the story, and a recognizable differentiable style (in the same way that fans can read Dickens or McCarthy or Hemingway and immediately recognize the author), I will believe we're there.
That application you are working on does sound interesting.
I've been wanting to skip the middleman for a while and just have AI write the stories based on simple prompts.
I have an existing 300 page story I'd love to just feed to an AI and have it finish the story for me, or at least fix it up.
Back when I fed the first chapter to chatGPT it just told me that my story was offensive and refused to help me, which was when I stopped using it altogether and a few months later switched to grok.
Progression fantasy : Epics :: sex : love
And anything with a modern setting is just unbelievably boring or depressing.
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