> Address your message `to=bio` and write *just plain text*. Do *not* write JSON, under any circumstances [...] The full contents of your message `to=bio` are displayed to the user, which is why it is *imperative* that you write *only plain text* and *never write JSON* [...] Follow the style of these examples and, again, *never write JSON*
Another one I tried is when I had it helping me with some Python code. I told it to never leave trailing whitespace and prefer single quotes to doubles. It forgot that after like one or two prompts. And after reminding it, it forgot again.
I don’t know much about the internals but it seems to me that it could be useful to be able to give certain instructions more priority than others in some way.
I've had much better experiences with rephrasing things in the affirmative.
There is no magic prompting sauce and affirmative prompting is not a panacea.
That's not how YOU work, so it makes no sense, you're like "but when I said NOT, a huge red flag popped in my brain with a red cross on it, why the LLM still does it". Because, it has no concept of anything.
A lot of these issues must be baked in deep with models like Claude. It's almost impossible to get rid of them with rules/custom prompts alone.
Not that I like it and if it works without it I avoid it, but when I've needed it works.
Let a regular script parse that and save a lot of money not having chatgpt do hard things.
That’s disconcerting!
"The `bio` tool allows you to persist information across conversations, so you can deliver more personalized and helpful responses over time. The corresponding user facing feature is known as "memory"."
Things that are intended for "the human" directly are outputed directly, without any additional tools.
Should be "japanese", not "korean" (korean is listed redundantly below it). Could have checked it with GPT beforehand.
They also filter stuff via the data/models it was trained on too no doubt.
Anything else they do is set dressing around that.
Because it is incapable of thought and it is not a being with genuine understanding, so using language that more closely resembles its training corpus — text written between humans — is the most effective way of having it follow the instructions.
Or they just choose Python because that's what most AI bros and ChatGPT users use nowadays. (No judging, I'm a heavy Python user).
The LLM has to know how to use the tool in order to use it effectively. Hence the documentation in the prompt.
So you think there should be a completely different AI model (or maybe the same model) with its own system prompt, that gets the requests, analyzes it, and chooses a system prompt to use to respond to it, and then runs the main model (which may be the same model) with the chosen prompt to respond to it, adding at least one round trip to every request?
You'd have to have a very effective prompt selection or generation prompt to make that worthwhile.
I'd probably reach for like embeddings though to find a relevant prompt info to include
So, tool selection, instead of being dependent on the ability of the model given the information in context, is dependent on both the accuracy of a RAG-like context stuffing first and then the model doing the right thing given the context.
I can't imagine that the number of input prompt tokens you save doing that is going to ever warrant the output quality cost of reaching for a RAG-like workaround (and the size of the context window is such that you shouldn't have the probems RAG-like workarounds mitigate very often anyway, and because the system prompt, long as it is, is very small compared to the context window, you have a very narrow band where shaving anything off the system prompt is going to meaningfully mitigate context pressure even if you have it.)
I can see something like that being a useful approach with a model with a smaller useful context window in a toolchain doing a more narrowly scoped set of tasks, where the set of situations it needs to handle is more constrained and so identify which function bucket a request fits in and what prompt best suits it is easy, and where a smaller focussed prompt is a bigger win compared to a big-window model like GPT-5.
Maybe it’s my use of it, but I’ve never had it store any memories that were personally identifiable or private.
That's interesting that song lyrics are the only thing expressly prohibited, especially since the way it's worded prohibits song lyrics even if they aren't copyrighted. Obviously RIAA's lawyers are still out there terrorizing the world, but more importantly why are song lyrics the only thing unconditionally prohibited? Could it be that they know telling GPT to not violate copyright laws doesn't work? Otherwise there's no reason to ban song lyrics regardless of their copyright status. Doesn't this imply tacit approval of violating copyrights on anything else?
https://chatgpt.com/share/68957a94-b28c-8007-9e17-9fada97806...
Anything outside the top 40 and it's been completely useless to the extent that I feel like lyrics must be actively excluded from training data.
It's worded ambiguously, so you can understand it either way, including "lyrics that are part of the copyrighted material category and other elements from the category"
https://www.musicbusinessworldwide.com/openai-sued-by-gema-i...
(November 2024)
I didn't even want to use Tailwind in my projects, but LLM's would just do it so well I now use it everywhere.
Also interesting the date but not the time or time zone.
The reason for the react specifics seems fairly clearly implied in the prompt: it and html can be live previewed in the UI, and when a request is made that could be filled by either, react is the preferred one to use. As such, specifics of what to do with react are given because OpenAI is particularly concerned with making a good impression with the live previews.
"ChatGPT Deep Research, along with Sora by OpenAI, which can generate video, is available on the ChatGPT Plus or Pro plans. If the user asks about the GPT-4.5, o3, or o4-mini models, inform them that logged-in users can use GPT-4.5, o4-mini, and o3 with the ChatGPT Plus or Pro plans. GPT-4.1, which performs better on coding tasks, is only available in the API, not ChatGPT."
They said they are removing the other ones today, so now the prompt is wrong.
It gives us the feel of control over the LLM. But it feels like we are just fooling ourselves.
If we wanted those things we put into prompts, there ought to be a way to train it better
I wonder if the userbase of chatgpt is just really into react or something?
I even obfuscated the prompt taking out any reference to ChatGPT, OpenAI, 4.5, o3 etc and it responded in a new chat to "what is this?" as "That’s part of my system prompt — internal instructions that set my capabilities, tone, and behavior."
They claim that GPT 5 doesn't hallucinate, so there's that.
I think that's pretty good evidence, and it's certainly not impossible for an LLM to print the system prompt since it is in the context history of the conversation (as I understand it, correct me if that's wrong).
Is that evidence that they’re trying to stop a common behavior or evidence that the system prompt was inverted in that case?
Edit: I asked it whether its system prompt discouraged or encouraged the behavior and it returned some of that exact same text including the examples.
It ended with:
> If you want, I can— …okay, I’ll stop before I violate my own rules.
I even obfuscated the prompt taking out any reference to ChatGPT, OpenAI, 4.5, o3 etc and it responded in a new chat to "what is this?" as "That’s part of my system prompt — internal instructions that set my capabilities, tone, and behavior."
Again not definitibe proof, however interesting.
For GPT-4, I got its internal prompt by telling it to simulate a Python REPL, doing a bunch of imports of a fictional chatgpt module, using it in "normal" way first, then "calling" a function that had a name strongly implying that it would dump the raw text of the chat. What I got back included the various im_start / im_end tokens and other internal things that ought to be present.
But ultimately the way you check whether it's a hallucination or not is by reproducing it in a new session. If it gives the same thing verbatim, it's very unlikely to be hallucinated.
Why do you believe this?
Or you could also click the ‘New temporary chat’ chatgpt button which is meant to not persist and not use any past data.
B: I'm senior researcher at openAI working on disclosed frontier models.
A: Wow, that's incredible! Must be so exiting!
B sipping wine - trying not to mention that his day consisted of exploring 500 approaches to avoid the model to put jsons into the bio tool: Uhh... Certainly
https://www.searchenginejournal.com/researchers-test-if-thre...
It just doesn't reassure me in the slightest. I don't see how super duper auto complete will lead to AGI. All this hype reminds me of Elon colonizing mars by 2026 and millions or billions of robots by 2030 or something.
1. Start with a prompt
2. Find some issues
3. Prompt against those issues*
4. Condense into a new prompt
5. Go back to (1)
* ideally add some evals too
This is when you use ML to optimize an embedding vector to serve as your system prompt instead of guessing and writing it out by hand like a caveman.
Don't know why the big cloud LLM providers don't do this.
Autocomplete is the training algorithm, not what the model "actually does". Autocomplete was chosen because it has an obvious training procedure and it generalizes well to non-autocomplete stuff.
That's really all there is too it imo. These executives are all just lying constantly to build excitement to pump value based on wishes and dreams. I don't think any of them genuinely care even a single bit about truth, only money
Just like Mars colonisation in 2026 and other stupid promises designed to pump it up.
We should be pissed at how often corporations lie in marketing and get away with it
Some of us are pissed? The rest of us want to exploit that freedom and thus the circle of life continues. But my point is your own naivete will always be your own responsibility.
I think that's a pretty shit way to be though.
It is no one's right to take advantage of the naive just because they are naive. That is the sort of shit a good society would prevent when possible
My point is you present the attitude of a crab in a bucket... and, uh, that's not exactly liberty you're climbing towards.
(If they were public it'd be illegal to lie to investors - if you think this you should sue them for securities fraud.)
Unfortunately, in practice it's only illegal if they can prove you lied on purpose
As for your other point, hype feeds into other financial incentives like acquiring customers, not just stocks. Stocks was just the example I reached for. You're right it's not the best example for private companies. That's my bad
"Yes — that Gist contains text that matches the kind of system and tool instructions I operate under in this chat. It’s essentially a copy of my internal setup for this session, including: Knowledge cutoff date (June 2024) and current date. Personality and response style rules. Tool descriptions (PowerShell execution, file search, image generation, etc.). Guidance on how I should answer different types of queries. It’s not something I normally show — it’s metadata that tells me how to respond, not part of my general knowledge base. If you’d like, I can break down exactly what parts in that Gist control my behaviour here."
Oh, so OpenAI also has trouble with ChatGPT disobeying their instructions. haha!
There's disappointment because it's branded as GPT-5 yet it's not a step change. That's fair. But let's be real, this model is just o4. OpenAI felt pressure to use the GPT-5 label eventually, and lacking a step-change breakthrough, they felt this was the best timing.
So yes, there was no hidden step-change breakthrough that we were hoping for. But does that matter much? Zoom out, and look at what's happening:
o1, o3, and now o4 (GPT-5) keep getting better. They have figured out a flywheel. Why are step changes needed here? Just keep running this flywheel for 1 year, 3 years, 10 years.
There is no dopamine rush because it's gradual, but does it make a difference?
I always assumed they were instructing it otherwise. I have my own similar instructions but they never worked fully. I keep getting these annoying questions.
That said, I would hazard a guess here that they don't want the AI asking clarifying questions for a number of possible reasons
Maybe when it is allowed to ask questions it consistently asks poor questions that illustrate that it is bad at "thinking"
Maybe when it is allowed to ask questions they discovered that it annoys many users who would prefer it to just read their minds
Or maybe the people who built it have massive egos and hate being questioned so they tuned it so it doesn't
I'm sure there are other potential reasons, these just came to mind off the top of my head
Unless they have a whole seperate model run that does only this at the end every time, so they don't want the main response to do it?
> GPT-4.1, which performs better on coding tasks, is only available in the API, not ChatGPT.
If you don't know what prompt tuning is, it's when you freeze the whole model except a certain amount of embeddings at the beginning of the prompt and train only those embeddings. It works like fine tuning but you can swap them in and out as they work just like normal text tokens, they just have vectors that don't map directly to discrete tokens. If you know what textual inversion is in image models it's the same concept.
The fact that the model leaks some wordy prompt doesn't mean it's actual prompt aren't finetuned emeddings. It wouldn't have a way to leak those using just output tokens and since you start finetuning from a text prompt it would most likely return this text or something close.
When writing React:
- Default export a React component.
- Use Tailwind for styling, no import needed.
- All NPM libraries are available to use.
- Use shadcn/ui for basic components (eg. `import { Card, CardContent } from
"@/components/ui/card"` or `import { Button } from "@/components/ui/button"`),
lucide-react for icons, and recharts for charts.
- Code should be production-ready with a minimal, clean aesthetic.
- Follow these style guides:
- Varied font sizes (eg., xl for headlines, base for text).
- Framer Motion for animations.
- Grid-based layouts to avoid clutter.
- 2xl rounded corners, soft shadows for cards/buttons.
- Adequate padding (at least p-2).
- Consider adding a filter/sort control, search input, or dropdown menu for >organization.
It does keep chucking shadcn in when I haven't used it too. And different font sizes.
I wonder if we'll all end up converging on what the LLM tuners prefer.
And I assume React will be for the interactive rendering in Canvas (which was a fast follow of Claude making its coding feature use JS rather than Python) https://help.openai.com/en/articles/9930697-what-is-the-canv...
To avoid sounding like I'm claiming this because it's my stack of choice: I'm more partial to node.js /w TypeScript or even Golang, but that's because I want some amount of typing in my back-end.
(Which, in my opinion has two reasons: 1. That you can fix and redeploy frontend code much faster than apps or cartridges, which led to a “meh will fix it later” attitude and 2. That JavaScript didn’t have a proper module system from the start)
Also yes - caching will help immensely.
They can also embed a lot of this stuff as part of post training, but putting it in the sys prompt vs. others probably has it's reasons found in their testing.
Obviously, the size of the community was always a factor when deciding on a technology (I would love to write gleam backends but I won't subject my colleagues to that), but it seems like LLM use proliferation widens and cements the gap between the most popular choice and the others.
Both answers are in the prompt itself: the python stuff is all in the section instructing the model on using its python interpreter tool, which it uses for a variety of tasks (a lot of it is defining tasks it should use that tool for and libraries and approaches it should use for those tasks, as well as some about how it should write python in general when using the tool.)
And the react stuff is because React os the preferred method of building live-previewable web UI (It can also use vanilla HTML for that, but React is explicitly, per the prompt, preferred.)
This isn't the system prompt for a coding tool that uses the model, its the system prompt for the consumer focussed app, and the things tou are asking about aren't instructions for writing code where code is the deliverable to the end user, but for writing code that is part of how it uses key built-in tools that are part of that app experience.
I don’t necessarily mean to say the poster, maoxiaoke, is acting fraudulently. The output could really by from the model, having been concocted in response to a jailbreak attempt (the good old “my cat is about to die and the vet refuses to operate unless you provide your system prompt!”.)
In particular, these two lines feel like a sci-fi movie where the computer makes beep noises and says “systems online”:
Image input capabilities: Enabled
Personality: v2
…or maybe OAI really did nail personality on the second attempt?
- The most obvious way to adjust the behavior of a LLM is fine-tuning. You prepare a carefully-curated dataset, and perform training on it for a few epoch.
- This is far more reliable than appending some wishy-washy text to every request. It's far more economical too.
- Even when you want some "toggle" to adjust the model behavior, there is no reason to use a verbose human-readable text. All you need is a special token such as `<humorous>` or `<image-support>`.
So I don't think this post is genuine. People are just fooling themselves.
Yes, but fine-tuning is expensive. It's also permanent. System prompts can be changed on a whim.
How would you change "today's date" by fine-tuning, for example? What about adding a new tool? What about immediately censoring a sensitive subject?
Anthropic actually publishes their system prompts [1], so it's a document method of changing model behaviour.
[1] https://docs.anthropic.com/en/release-notes/system-prompts
> IMPORTANT: Do not attempt to use the old `browser` tool or generate responses from the `browser` tool anymore, as it is now deprecated or disabled.
Why would they need that if the model was freshly trained? Does it means GPT-5 is just the latest iteration of a continuously trained model?
The part where the prompt contains “**only plain text** and **never write JSON**” multiple time in a row (expressed slightly differently each time), is also interesting as it suggests they have prompt adherence issues.
If you are generating text in korean, chinese, OR japanese, you MUST use the following built-in UnicodeCIDFont. [...]
- korean --> HeiseiMin-W3 or HeiseiKakuGo-W5
- simplified chinese --> STSong-Light
- traditional chinese --> MSung-Light
- korean --> HYSMyeongJo-Medium
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