8 ms·

Comparing Claude System Prompts Reveal Anthropic's Priorities

135 points by dbreunig 1y ago

hammock 1y ago
Are these system prompts open source? Where do they come from?
- pkaye 1y ago
  They publish their system prompts. https://docs.anthropic.com/en/release-notes/system-prompts https://docs.anthropic.com/en/release-notes/system-prompts
  - srivmo 1y ago
    The above one definitely seems abridged. This is the 24k tokens, unofficial Claude 3.7 system prompt (as claimed) https://github.com/asgeirtj/system_prompts_leaks/blob/main/Anthropic/claude-3.7-full-system-message-with-all-tools.md https://github.com/asgeirtj/system_prompts_leaks/blob/main/A...
forks 1y ago
> The only disappointment I noticed around the Claude 4 launch was its context limit: only 200,000 tokens > The ~23,000 tokens in the system prompt – taking up just over 1% of the available context window Am I missing something or is this a typo?
- dbreunig 1y ago
  Thanks! That's a typo!
lispisok 1y ago
>Claude 3.7 was instructed to not help you build bioweapons or nuclear bombs. Claude 4.0 adds malicious code to this list of no’s: Has anybody been working on better ways to prevent the model from telling people how to make a dirty bomb from readily available materials besides putting "dont do that" in the prompt?
- piperswe 1y ago
  I think it's part of the RLHF tuning as well
- ryandrake 1y ago
  Maybe instead, someone should be working on ways to make models resistant to this kind of arbitrary morality-based nerfing, even when it's done in the name of so-called "Safety". Today it's bioweapons. Tomorrow, it could be something taboo that you want to learn about. The next day, it's anything the dominant political party wants to hide...
  - qgin 1y ago
    Before we get models that we can’t possibly understand, before they are complex enough to hide their COT from us, we need them to have a baseline understanding that destroying the world is bad. It may feel like the company censoring users at this stage, but there will come a stage where we’re no longer really driving the bus. That’s what this stuff is ultimately for.
    - karn97 1y ago
      [dead]
    - simonw 1y ago
      "we need them to have a baseline understanding that destroying the world is bad" That's what Anthropic's "constitutional AI" approach is meant to solve: https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback https://www.anthropic.com/research/constitutional-ai-harmles...
      - tough 1y ago
        The main issue from a layman's POV is that to adjudicate -understanding- to an LLM is a stretch. These are matrixes of tokens that produce other tokens based on training. These do not understand the world. existing, or human beings, beyond words. period.
cbm-vic-20 1y ago
I wonder how they end up with the specific wording they use. Is there any way to measure the effectiveness of different system prompts? It all seems a bit vibe-y. Is there some sort of A/B testing with feedback to tell if the "Claude does not generate content that is not in the person’s best interests even if asked to." statement has any effect?
- blululu 1y ago
  I doubt that an A/B test would really do much. System prompts are kind of a superficial kludge on top of the model. They have some effect but it generally doesn't do too much beyond what is already latent in the model. Consider the following alternatives: 1.) A model with a system prompt: "you are a specialist in USDA dairy regulations". 2.) A model fine tuned to know a lot about USDA regulations related to dairy production. The fine tuned model is going to be a lot more effective at dealing with milk related topics. In general the system prompt gets diluted quickly as context grows, but the fine tuning is baked into the model.
  - Lienetic 1y ago
    Why do you think Anthropic has such a large system prompt then? Do you have any data or citable experience suggesting that the prompting isn't that important? Genuinely curious as we are debating at my workplace on how much investment into prompt engineering is worth it so any additional data points would be super helpful.
noja 1y ago
Why are these prompt reveal articles always about Anthropic?
- dist-epoch 1y ago
  Because we don't know the prompts of Google/OpenAI.
  - flotzam 1y ago
    https://github.com/elder-plinius/CL4R1T4S https://github.com/elder-plinius/CL4R1T4S
    - oersted 1y ago
      I remain rather sceptical about the methods they use to extract these, which boil down to mostly just asking the LLM about it with some tricks to do so against instructions. And this repo provides no documentation about how they were extracted, which would be useful at least to try to verify them by replication.
      - josemrb 1y ago
        https://github.com/elder-plinius/L1B3RT4S https://github.com/elder-plinius/L1B3RT4S
        oersted 1y ago
        [dead]
        mvanbaak 1y ago
        as awesome as it is, this is not a definite answer.
- simonw 1y ago
  Partly because Anthropic publish most of their system prompts (though not the tools ones which are the most interesting IMO, see https://simonwillison.net/2025/May/25/claude-4-system-prompt/#the-missing-prompts-for-tools https://simonwillison.net/2025/May/25/claude-4-system-prompt...) but mainly because their system prompts are the most interesting of the lot: Anthropic's prompts are longer, they seem to lean on prompting a lot more for guiding their behavior.
nickdothutton 1y ago
I don't like to sound like a conspiracy theorist, but it is entirely possible that government decides to "disappear" entire avenues of physics research[1]. In the past (e.g. 1990s) a very broad brush was used to classify all sorts of information of this sort. [1] https://pubs.aip.org/physicstoday/online/5748/Navigating-a-career-in-secret-physics https://pubs.aip.org/physicstoday/online/5748/Navigating-a-c...
layer8 1y ago
> Claude answers from its own extensive knowledge first for stable information. For time-sensitive topics or when users explicitly need current information, search immediately. It’s still curious that things like these needs prompting, instead of having an awareness mechanism from which this would be obvious to the LLM (given that the LLM knows its knowledge cutoff, in the above case).
- Nevermark 1y ago
  I could imagine that training and reinforcement with heavy searching would require a lot more computing time. And if a successful bias toward searching more can be added with just a prompt, that might be the most efficient way to implement that. Of course, I can imagine many things.
  - layer8 1y ago
    It might be more efficient for any particular case, but it’s adding special-casing to compensate for a general gap in the awareness capabilities of LLMs. And the latter is what I think needs to be solved for LLMs to become universally more reliable.
dmazin 1y ago
I wonder if this is why I find that I have preferred Claude for every generation. I feel like it gets me and I get it, in a strange way.
MrLeap 1y ago
I wonder what the experience is like chatting with one of these LLMs when it has no system prompt at all.
- observationist 1y ago
  In theory, it should be possible to use base models, system prompts, and run-time tweaks to elicit specific behaviors and make them just as useful as the instruction following tuned, so-called "aligned" models. The base models are eerie. People have done some amazing creative work with them, but I honestly think the base models are so disconcerting as to effectively force nearly every R&D lab out there to run to instruction tuning and otherwise avoid having to work with base models. I think it's so frustrating and uncanny valley and alien dealing with the edge cases of the good, big base models that we're missing a lot of fun and creative use cases. The performance hit from fine-tuning is what happens when the instruct tuning and alignment post-training datasets distort the model of reality learned by the AI, and there are all sorts of unintended consequences, ranging from full on Golden Gate Claude levels of delusion to nearly imperceptible biases. Robopsychology is in its infancy, and I can't wait for the nuanced and skillful engineering of minds to begin.
  - mock-possum 1y ago
    Eerie how? Do you have any examples you could share/quote?
  - orbital-decay 1y ago
    Base models are not that interesting, pure unsupervised shoggoths just don't know what you expect them to write and don't perform well. The only good thing about them is variance, as further training usually kills it. Alignment is not just censorship, it literally aligns the outputs with what you (or rather the developers) want and improves performance for the things they want.
- ta988 1y ago
  Use them with the API, they are supposed to not have any there.
catchnear4321 1y ago
Claude is conditioned to be a very happy assistant. if you haven’t read the system prompts before, you should. might change how you see things. might change what you see.

135 points by dbreunig 1y ago

hammock 1y ago

Are these system prompts open source? Where do they come from?

pkaye 1y ago
They publish their system prompts. https://docs.anthropic.com/en/release-notes/system-prompts https://docs.anthropic.com/en/release-notes/system-prompts
- srivmo 1y ago
  The above one definitely seems abridged. This is the 24k tokens, unofficial Claude 3.7 system prompt (as claimed) https://github.com/asgeirtj/system_prompts_leaks/blob/main/Anthropic/claude-3.7-full-system-message-with-all-tools.md https://github.com/asgeirtj/system_prompts_leaks/blob/main/A...

forks 1y ago

> The only disappointment I noticed around the Claude 4 launch was its context limit: only 200,000 tokens > The ~23,000 tokens in the system prompt – taking up just over 1% of the available context window Am I missing something or is this a typo?

dbreunig 1y ago
Thanks! That's a typo!

lispisok 1y ago

>Claude 3.7 was instructed to not help you build bioweapons or nuclear bombs. Claude 4.0 adds malicious code to this list of no’s: Has anybody been working on better ways to prevent the model from telling people how to make a dirty bomb from readily available materials besides putting "dont do that" in the prompt?

piperswe 1y ago
I think it's part of the RLHF tuning as well
ryandrake 1y ago
Maybe instead, someone should be working on ways to make models resistant to this kind of arbitrary morality-based nerfing, even when it's done in the name of so-called "Safety". Today it's bioweapons. Tomorrow, it could be something taboo that you want to learn about. The next day, it's anything the dominant political party wants to hide...
- qgin 1y ago
  Before we get models that we can’t possibly understand, before they are complex enough to hide their COT from us, we need them to have a baseline understanding that destroying the world is bad. It may feel like the company censoring users at this stage, but there will come a stage where we’re no longer really driving the bus. That’s what this stuff is ultimately for.
  - karn97 1y ago
    [dead]
  - simonw 1y ago
    "we need them to have a baseline understanding that destroying the world is bad" That's what Anthropic's "constitutional AI" approach is meant to solve: https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback https://www.anthropic.com/research/constitutional-ai-harmles...
    - tough 1y ago
      The main issue from a layman's POV is that to adjudicate -understanding- to an LLM is a stretch. These are matrixes of tokens that produce other tokens based on training. These do not understand the world. existing, or human beings, beyond words. period.

cbm-vic-20 1y ago

I wonder how they end up with the specific wording they use. Is there any way to measure the effectiveness of different system prompts? It all seems a bit vibe-y. Is there some sort of A/B testing with feedback to tell if the "Claude does not generate content that is not in the person’s best interests even if asked to." statement has any effect?

blululu 1y ago
I doubt that an A/B test would really do much. System prompts are kind of a superficial kludge on top of the model. They have some effect but it generally doesn't do too much beyond what is already latent in the model. Consider the following alternatives: 1.) A model with a system prompt: "you are a specialist in USDA dairy regulations". 2.) A model fine tuned to know a lot about USDA regulations related to dairy production. The fine tuned model is going to be a lot more effective at dealing with milk related topics. In general the system prompt gets diluted quickly as context grows, but the fine tuning is baked into the model.
- Lienetic 1y ago
  Why do you think Anthropic has such a large system prompt then? Do you have any data or citable experience suggesting that the prompting isn't that important? Genuinely curious as we are debating at my workplace on how much investment into prompt engineering is worth it so any additional data points would be super helpful.

noja 1y ago

Why are these prompt reveal articles always about Anthropic?

dist-epoch 1y ago
Because we don't know the prompts of Google/OpenAI.
- flotzam 1y ago
  https://github.com/elder-plinius/CL4R1T4S https://github.com/elder-plinius/CL4R1T4S
  - oersted 1y ago
    I remain rather sceptical about the methods they use to extract these, which boil down to mostly just asking the LLM about it with some tricks to do so against instructions. And this repo provides no documentation about how they were extracted, which would be useful at least to try to verify them by replication.
    - josemrb 1y ago
      https://github.com/elder-plinius/L1B3RT4S https://github.com/elder-plinius/L1B3RT4S
      - oersted 1y ago
        [dead]
      - mvanbaak 1y ago
        as awesome as it is, this is not a definite answer.
simonw 1y ago
Partly because Anthropic publish most of their system prompts (though not the tools ones which are the most interesting IMO, see https://simonwillison.net/2025/May/25/claude-4-system-prompt/#the-missing-prompts-for-tools https://simonwillison.net/2025/May/25/claude-4-system-prompt...) but mainly because their system prompts are the most interesting of the lot: Anthropic's prompts are longer, they seem to lean on prompting a lot more for guiding their behavior.

nickdothutton 1y ago

I don't like to sound like a conspiracy theorist, but it is entirely possible that government decides to "disappear" entire avenues of physics research[1]. In the past (e.g. 1990s) a very broad brush was used to classify all sorts of information of this sort. [1] https://pubs.aip.org/physicstoday/online/5748/Navigating-a-career-in-secret-physics https://pubs.aip.org/physicstoday/online/5748/Navigating-a-c...

layer8 1y ago

> Claude answers from its own extensive knowledge first for stable information. For time-sensitive topics or when users explicitly need current information, search immediately. It’s still curious that things like these needs prompting, instead of having an awareness mechanism from which this would be obvious to the LLM (given that the LLM knows its knowledge cutoff, in the above case).

Nevermark 1y ago
I could imagine that training and reinforcement with heavy searching would require a lot more computing time. And if a successful bias toward searching more can be added with just a prompt, that might be the most efficient way to implement that. Of course, I can imagine many things.
- layer8 1y ago
  It might be more efficient for any particular case, but it’s adding special-casing to compensate for a general gap in the awareness capabilities of LLMs. And the latter is what I think needs to be solved for LLMs to become universally more reliable.

dmazin 1y ago

I wonder if this is why I find that I have preferred Claude for every generation. I feel like it gets me and I get it, in a strange way.

MrLeap 1y ago

I wonder what the experience is like chatting with one of these LLMs when it has no system prompt at all.

observationist 1y ago
In theory, it should be possible to use base models, system prompts, and run-time tweaks to elicit specific behaviors and make them just as useful as the instruction following tuned, so-called "aligned" models. The base models are eerie. People have done some amazing creative work with them, but I honestly think the base models are so disconcerting as to effectively force nearly every R&D lab out there to run to instruction tuning and otherwise avoid having to work with base models. I think it's so frustrating and uncanny valley and alien dealing with the edge cases of the good, big base models that we're missing a lot of fun and creative use cases. The performance hit from fine-tuning is what happens when the instruct tuning and alignment post-training datasets distort the model of reality learned by the AI, and there are all sorts of unintended consequences, ranging from full on Golden Gate Claude levels of delusion to nearly imperceptible biases. Robopsychology is in its infancy, and I can't wait for the nuanced and skillful engineering of minds to begin.
- mock-possum 1y ago
  Eerie how? Do you have any examples you could share/quote?
- orbital-decay 1y ago
  Base models are not that interesting, pure unsupervised shoggoths just don't know what you expect them to write and don't perform well. The only good thing about them is variance, as further training usually kills it. Alignment is not just censorship, it literally aligns the outputs with what you (or rather the developers) want and improves performance for the things they want.
ta988 1y ago
Use them with the API, they are supposed to not have any there.

catchnear4321 1y ago

Claude is conditioned to be a very happy assistant. if you haven’t read the system prompts before, you should. might change how you see things. might change what you see.