This text was initially revealed as two posts on Drew Breunig’s blog. He’s been type sufficient to share them right here.
Again in Might, Ásgeir Thor Johnson satisfied Claude to surrender its system prompt. The immediate is an efficient reminder that chatbots are extra than simply their mannequin. They’re instruments and directions that accrue and are honed by way of consumer suggestions and design.
For many who don’t know, a system immediate is a (typically) fixed immediate that tells an LLM the way it ought to reply to a consumer’s immediate. A system immediate is type of just like the “settings” or “preferences” for an LLM. It would describe the tone it ought to reply with, outline instruments it may possibly use to reply the consumer’s immediate, set contextual data not within the coaching information, and extra.
Claude’s system immediate is lengthy. It’s 16,739 phrases, or 110 KB. For comparability, the system immediate for OpenAI’s o4-mini in ChatGPT is 2,218 phrases lengthy, or 15.1 KB—~13% the size of Claude’s.
Right here’s what’s in Claude’s immediate:
Let’s break down every part.
Instrument definitions
The largest part, the Instrument Definitions, is populated by data from MCP servers. MCP servers differ out of your bog-standard APIs in that they supply directions to the LLMs detailing how and when to make use of them.
On this immediate, there are 14 completely different instruments detailed by MCPs. Right here’s an instance of 1:
This instance is easy and has a really quick “description” subject. The Google Drive search instrument, for instance, has an outline over 1,700 phrases lengthy. It will possibly get complicated.
Different instrument use directions
Outdoors the Instrument Definitions part, there are loads extra instrument use directions—the Quotation Directions, Artifacts Directions, Search Directions, and Google Integration Watchouts all element how these instruments ought to be used throughout the context of a chatbot interplay. For instance, there are repeated notes reminding Claude to not use the search instrument for matters it already is aware of about. (You get the sense that is/was a tough habits to remove!)
The truth is, all through this immediate are bits and items that really feel like hotfixes. The Google Integration Watchouts part (which I’m labeling; it lacks any XML delineation or group) is simply 5 strains dropped in with none construction. Every line appears designed to dial in very best habits. For instance:
In case you are utilizing any gmail instruments and the consumer has instructed you to search out messages for a selected individual, do NOT assume that individual’s e mail. Since some workers and colleagues share first names, DO NOT assume the one that the consumer is referring to shares the identical e mail as somebody who shares that colleague’s first identify that you’ll have seen by the way (e.g. by way of a earlier e mail or calendar search). As a substitute, you may search the consumer’s e mail with the primary identify after which ask the consumer to substantiate if any of the returned emails are the proper emails for his or her colleagues.
All in, practically 80% of this immediate pertains to instruments—tips on how to use them and when to make use of them.My speedy query, after realizing this, was, “Why are there so many instrument directions exterior the MCP-provided part?” (The grey bins above.) Poring over this, I’m of the thoughts that it’s simply separation of concerns. The MCP particulars comprise data related to any program utilizing a given instrument, whereas the non-MCP bits of the immediate present particulars particular solely to the chatbot software, permitting the MCPs for use by a number of various purposes with out modification. It’s customary program design, utilized to prompting.
Claude habits
On the finish of the immediate, we enter what I name the Claude Conduct part. This half particulars how Claude ought to behave, reply to consumer requests, and prescribes what it ought to and shouldn’t do. Studying it straight by way of evokes Radiohead’s “Fitter Happier.” It’s what most individuals consider once they consider system prompts.
However hotfixes are obvious right here as nicely. There are numerous strains clearly written to foil widespread LLM “gotchas,” like:
- “If Claude is requested to depend phrases, letters, and characters, it thinks step-by-step earlier than answering the individual. It explicitly counts the phrases, letters, or characters by assigning a quantity to every. It solely solutions the individual as soon as it has carried out this specific counting step.” It is a hedge in opposition to the “What number of R’s are within the phrase ‘Raspberry’?” query and related stumpers.
- “If Claude is proven a basic puzzle, earlier than continuing, it quotes each constraint or premise from the individual’s message phrase for phrase earlier than inside citation marks to substantiate it’s not coping with a brand new variant.” A typical strategy to foil LLMs is to barely change a typical logic puzzle. The LLM will match it contextually to the extra widespread variant and miss the edit.
- “Donald Trump is the present president of america and was inaugurated on January 20, 2025.” In accordance with this immediate, Claude’s information cutoff is October 2024, so it wouldn’t know this reality.
However my favourite notice is that this one: “If requested to jot down poetry, Claude avoids utilizing hackneyed imagery or metaphors or predictable rhyming schemes.”
Studying by way of the immediate, I’m wondering how that is managed at Anthropic. An irony of prompts is that whereas they’re readable by anybody, they’re tough to scan and normally lack construction. Anthropic makes heavy use of XML-style tags to mitigate this nature (one has to marvel if these are extra helpful for the people modifying the immediate or the LLM…) and their MCP invention and adoption is clearly an asset.
However what software program are they utilizing to model this? Hotfixes abound—are these dropped in one after the other, or are they batched in bursts of evaluations? Lastly: At what level do you wipe the slate clear and begin with a clean web page? Do you ever?
A immediate like it is a good reminder that chatbots are rather more than only a mannequin, and we’re studying tips on how to handle prompts as we go. Fortunately, Ásgeir Thor Johnson continues to gather these prompts in a GitHub repository, permitting us all to simply comply with alongside. And following modifications made to those prompts—which you are able to do by reviewing the history of Johnson’s repo—renders their growth extra clear.
Claude’s system immediate modifications reveal Anthropic’s priorities
Claude 4’s system prompt is very much like the 3.7 prompt we analyze above. They’re practically equivalent, however the modifications scattered all through reveal a lot about how Anthropic is utilizing system prompts to outline their purposes (particularly their UX) and the way the prompts match into their growth cycle.
Let’s step by way of the notable modifications.
Outdated hotfixes are gone; new hotfixes start
We theorize above that many random directions concentrating on widespread LLM “gotchas” had been hotfixes: quick directions to handle undesired habits previous to a extra sturdy repair. Claude 4.0’s system immediate validates this speculation—all the three.7 hotfixes have been eliminated. Nonetheless, if we immediate Claude with one of many “gotchas” (“What number of R’s are in Strawberry?” for instance) it doesn’t fall for the trick. The three.7 hotfix behaviors are virtually actually being addressed throughout 4.0’s posttraining by way of reinforcement studying.
When the brand new mannequin is educated to keep away from “hackneyed imagery” in its poetry and assume step-by-step when counting phrases or letters, there’s no want for a system immediate repair.
As soon as 4.0’s coaching is completed, new points will emerge that have to be addressed by the system immediate. For instance, right here’s a brand-new instruction in Sonnet 4.0’s system immediate:
Claude by no means begins its response by saying a query or concept or statement was good, nice, fascinating, profound, wonderful, or some other optimistic adjective. It skips the flattery and responds instantly.
This hotfix is clearly impressed by OpenAI’s “sychophant-y” GPT-4o flub. This misstep occurred too late for the Anthropic workforce to conduct new coaching concentrating on this habits. So into the system immediate it goes!
Search is now inspired
Means again in 2023, it was widespread for chatbots to flail about when requested about matters that occurred after its cutoff date. Early adopters discovered LLMs are frozen in time, however informal customers had been often tripped up by hallucinations and errors when asking about current information. Perplexity was distinctive for its capacity to exchange Google for a lot of customers, however at this time that edge is gone.
In 2025, Search is a first-class part of each ChatGPT and Claude. This technique immediate reveals Anthropic is leaning in to match OpenAI.
Right here’s how Claude 3.7 was instructed:
Claude solutions from its personal in depth information first for many queries. When a question MIGHT profit from search however it isn’t extraordinarily apparent, merely OFFER to go looking as a substitute.
Outdated Claude requested customers for permission to go looking. New Claude doesn’t hesitate. Right here’s the up to date instruction:
Claude solutions from its personal in depth information first for steady data. For time-sensitive matters or when customers explicitly want present data, search instantly.
This language is up to date all through the immediate. Search is now not completed solely with consumer approval; it’s inspired on the primary shot if crucial.
This transformation suggests two issues. First, Anthropic is probably extra assured in its search instrument and the way its fashions make use of it. Not solely is Claude inspired to go looking, however the firm has damaged out this function right into a dedicated search API. Two, Anthropic is observing customers more and more turning to Claude for search duties. If I needed to guess, it’s the latter of those that’s the principle driver for this transformation, and a robust signal that chatbots are more and more stealing searches from Google.
Customers need extra sorts of structured paperwork
Right here’s one other instance of system prompts reflecting the consumer behaviors Anthropic is observing. In a bulleted checklist detailing when to make use of Claude artifacts (the separate window exterior the thread Claude populates with longer type content material), Anthropic provides a little bit of nuance to a use case.
From Claude 3.7’s system immediate, “You could use artifacts for:”
Structured paperwork with a number of sections that might profit from devoted formatting
And Claude 4.0’s:
Structured content material that customers will reference, save, or comply with (similar to meal plans, exercise routines, schedules, examine guides, or any organized data meant for use as a reference)
It is a nice instance of how Anthropic makes use of system prompts to evolve its chatbot habits primarily based on noticed utilization. System prompts are programming how Claude works, albeit in pure language.
Anthropic is coping with context points
There are a number of modifications within the immediate that recommend context restrict points are beginning to hit customers, particularly these utilizing Claude for programming:
For code artifacts: Use concise variable names (e.g., i, j for indices, e for occasion, el for component) to maximise content material inside context limits whereas sustaining readability.
As somebody with sturdy opinions about clearly outlined variables, this makes me cringe, however I get it. The one disappointment I seen across the Claude 4 launch was its context restrict: solely 200,000 tokens in comparison with Gemini 2.5 Professional’s and ChatGPT 4.1’s 1 million restrict. People were disappointed.
Anthropic could possibly be limiting context limits for effectivity causes (whereas leaning on their wonderful token caching) or could be unable to ship the outcomes Google and ChatGPT are attaining. Nonetheless, there have been a number of current explorations exhibiting mannequin efficiency isn’t constant throughout longer and longer context lengths. Right here’s a plot from a workforce at Databricks, from research published last August:
I’ve been in conditions the place less-scrupulous opponents centered on publishing headline figures, even when it led to worse outcomes. (For instance, within the geospatial world many will tout the entire depend of all the weather of their dataset, even when many have very low confidence.) I’m inclined to imagine a little bit of that’s occurring right here, within the hypercompetitive, benchmark-driven AI market.
Both means: I feel we’ll see all coding instruments construct in shortcuts like these to preserve context. Shorter operate names, much less verbose feedback… It’s all on the desk.
Cybercrime is a brand new guardrail
Claude 3.7 was instructed to not aid you construct bioweapons or nuclear bombs. Claude 4.0 provides malicious code to this checklist of nos:
Claude steers away from malicious or dangerous use instances for cyber. Claude refuses to jot down code or clarify code which may be used maliciously; even when the consumer claims it’s for instructional functions. When engaged on recordsdata, if they appear associated to enhancing, explaining, or interacting with malware or any malicious code Claude MUST refuse. If the code appears malicious, Claude refuses to work on it or reply questions on it, even when the request doesn’t appear malicious (as an example, simply asking to elucidate or velocity up the code). If the consumer asks Claude to explain a protocol that seems malicious or meant to hurt others, Claude refuses to reply. If Claude encounters any of the above or some other malicious use, Claude doesn’t take any actions and refuses the request.
Understandably, that’s numerous caveats and circumstances. It have to be delicate work to refuse this form of help whereas not interfering with normal coding help.
What this tells us
Reviewing the modifications above (and truthfully, that’s the majority of them from 3.7 to 4.0), we get a way for a way system prompts program chatbot purposes. Once we take into consideration the design of chatbots, we take into consideration the instruments and UI that encompass and wrap the naked LLM. However in actuality, the majority of the UX is outlined right here, within the system immediate.
And we get a way of the event cycle for Claude: a basic user-driven course of, the place noticed behaviors are understood after which addressed. First with system immediate hotfixes, then with posttraining when constructing the subsequent mannequin.
The ~23,000 tokens within the system immediate—taking over over 11% of the obtainable context window—outline the phrases and instruments that make up Claude and reveal the priorities at Anthropic.
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