> a contrast between Claude’s modern approach [...] XML, a technology dating back to 1998
Are we really at the point where some people see XML as a spooky old technology? The phrasing dotted around this article makes me feel that way. I find this quite strange.
XML has been "spooky old technology" for over a decade now. It's heyday was something like 2002.
Nobody dares advertise the XML capabilities of their product (which back then everybody did), nobody considers it either hot new thing (like back then) or mature - just obsolete enterprise shit.
It's about as popular now as J2EE, except to people that think "10 years ago" means 1999.
It's not the hot new thing but when has hype ever mattered for getting shit done? I don't think anyone who considers it obsolete has an informed opinion on the matter.
Typically a more primitive (sorry, minimal) format such as JSON is sufficient in which case there's no excuse to overcomplicate things. But sometimes JSON isn't sufficient and people start inventing half baked solutions such as JSON-LD for what is already a solved problem with a mature tech stack.
XSLT remains an elegant and underused solution. Guile even includes built in XML facilities named SXML.
HTML is actually a dialect of SGML. XHTML was an attempt to move to an XML-based foundation, but XML's strictness in parsing worked against it, and eventually folks just standardized how HTML parsers should interpret ill-formed HTML instead.
No, HTML was historically supposed to be a subset of SGML; XML is also an application of SGML. XHTML is the XML version of HTML. As of HTML5, HTML is no longer technically SGML or XML.
For me, even when it was first released, I considered obsolete enterprise shit. That view has not diminished as the sorry state of performance and security in that space has just reaffirmed that perception.
The use of XML as a data serialization format was always a bad choice. It was designed as a document _markup_ language (it’s in the name), which is exactly the way it’s being used for Claude, and is actually a good use case.
If you think XML is old tech, wait until you hear of EDI, still powering Walmart and Amazon logistics. XML came in like a wrecking ball with its self-documenting promise designed to replace that cryptic pesky payload called EDI. XML promised to solve world hunger. It spawned SOAP, XML over RPC, DOM, DTD, the heyday was beautiful and Microsoft was leading the charge. C# was also right around this time. Consulting firms were bloomed charged with delivering the asynchronous revolution, the loosely coupled messaging promises of XML.
I think it succeeded and it’s now quietly in the halls of warehouse having a beer or two with its older cousin the Electronic Data Interchange aka EDI.
The evidence suggests that XML was never that popular though for the general audience, you have to admit.
For Web markup, as an industry we tried XHTML (HTML that was strictly XML) for a while, and that didn't stick, and now we have HTML5 which is much more lenient as it doesn't even require closing tags in some cases.
For data exchange, people vastly prefer JSON as an exchange format for its simplicity, or protobuf and friends for their efficiency.
As a configuration format, it has been vastly overtaken by YAML, TOML, and INI, due to their content-forward syntax.
Having said all this I know there are some popular tools that use XML like ClickHouse, Apple's launchd, ROS, etc. but these are relatively niche compared to (e.g.) HTML
MS Office and Open-/LibreOffice are using zipped xml files (e.g. .docx, .xlsx and .odt). Svg vector graphics is xml, the x in ajax stands for xml (although replaced by json by now). SOAP (probably counts as the predecessor of REST) is xml-based.
XML was definitely popular in the "well used" sense. How popular it was in the "well liked" sense can maybe be up for debate, but it was the best tool for the job at the time for alot of use cases.
The thesis here seems to be that delimiters provide important context for Claude, and for that putpose we should use XML.
The article even references English's built-in delimiter, the quotation mark, which is reprented as a token for Claude, part of its training data.
So are we sure the lesson isn't simply to leverage delimiters, such as quotation marks, in prompts, period? The article doesn't identify any way in which XML is superior to quotation marks in scenarios requiring the type of disambiguation quotation marks provide.
Rather, the example XML tags shown seem to be serving as a shorthand for notating sections of the prompt ("treat this part of the prompt in this particular way"). That's useful, but seems to be addressing concerns that are separate from those contemplated by the author.
I'm sure Claude can handle any delimiter and pseudo markup you throw at it, but one benefit of XML delimiters over quotation marks is that you repeat the delimiter name at the end, which I'd imagine might help if its contents are long (it certainly helps humans).
The GP isn't suggesting to literally use quotes as the delimiter when prompting LLMs. They're pointing out that we humans already use delimiters in our natural language (quotation marks to delimit quotes). They're suggesting that delimiters of any kind may be helpful in the context of LLM prompting, which to me makes intuitive sense. That Claude is using XML is merely a convention.
But should this extend to anything that could end up in Claudes context? Should we be using xml even in skills for instance, or commands, custom subagents etc.
And then do we end up over indexing on Claude and maybe this ends up hurting other models for those using multiple tools.
I just dislike how much of AI is people saying "do this thing for better results" with no definitive proof but alas it comes with the non determinism.
At least this one has the stamp of approval by Claude codes team itself.
This matches my experience building AI-powered analysis tools. Structured output from LLMs is dramatically more reliable when you give the model clear delimiters to work with.
One thing I've found: even with XML tags, you still need to validate and parse defensively. Models will occasionally nest tags wrong, omit closing tags, or hallucinate new tag names. Having a fallback parser that extracts content even from malformed XML has saved me more than once.
The real win is that XML tags give you a natural way to do few-shot prompting with structure. You can show the model exactly what shape the output should take, and it follows remarkably well.
It'd be hilarious if XML schemas and validators were to make a comeback [1] to... interface with AI models.
[1] well of course XML is still heavily used in stuff like interfacing with automated wire transfers with big banks (at least in Europe) and all the digital payments directives etc. But XML is not widely used by the "cool" stuff.
Total tangent, but what vagary of HTML (or the Brave Browser, which I'm using here) causes words to be split in very odd places? The "inspect" devtools certainly didn't show anything unusual to me. (Edit: Chrome, MS Edge, and Firefox do the same thing. I also notice they're all links; wonder if that has something to do with it.)
To me it seems like handling symbols that start and end sequences that could contain further start and end symbols is a difficult case.
Humans can't do this very well either, we use visual aids such as indentation, synax hilighting or resort to just plain counting of levels.
Obviously it's easy to throw parameters and training at the problem, you can easily synthetically generate all the XML training data you want.
I can't help but think that training data should have a metadata token per content token. A way to encode the known information about each token that is not represented in the literal text.
Especially tagging tokens explicitly as fiction, code, code from a known working project, something generated by itself, something provided by the user.
While it might be fighting the bitter lesson, I think for explicitly structured data there should be benefits. I'd even go as far to suggest the metadata could handle nesting if it contained dimensions that performed rope operations to keep track of the depth.
If you had such a metadata stream per token there's also the possibility of fine tuning instruction models to only follow instructions with a 'said by user' metadata, and then at inference time filter out that particular metadata signal from all other inputs.
It seems like that would make prompt injection much harder.
Basically, the only way you're separting user input from model meta-input is using some kind of character that'll never show up in the output of either users or LLMs.
While technically possible, it'd be like a unicode conspiracy that had to quietly update everywhere without anyone being the wiser.
My intuition is it comes down to error-correcting codes. We're dealing with lossy systems that get off track, so including parity bits helps.
Ex: <message>...</message> helps keep track. Even better? <message78>...</message78>. That's ugly xml, but great for LLMs. Likewise, using standard ontologies for identifiers (ex: we'll do OCSF, AT&CK, & CIM for splunk/kusto in louie.ai), even if they're not formally XML.
For all these things... these intuitions need backing by evals in practice, and part of why I begrudgingly flipped from JSON to XML
I think XML is good to know for prompting (similar to how <think></think> was popular for outputs, you can do that for other sections). But I have had much better experience just writing JSON and using line breaks, colons, etc. to demarcate sections.
XML helps because it a) Lets you to describe structures b) Make a clear context-change which make it clear you are not "talking in XML" you are "talking about XML".
I assume you are right too, JSON is a less verbose format which allows you to express any structure you can express in XML, and should be as easy for AI to parse. Although that probably depends on the training data too.
I recently asked AI why .md files are so prevalent with agentic AI and the answer is ... because .md files also express structure, like headers and lists.
Again, depends on what the AI has been trained on.
I would go with JSON, or some version of it which would also allow comments.
Could you clarify, do those tags need to be tags which exist and we need to lear about them and how to use them? Or we can put inside them whatever we want and just by virtue of being tags, Claude understands them in a special way?
All the major foundation models will understand them implicitly, so it was popular to use <think>, but you could also use <reason> or <thinkhard> and the model would still go through the same process.
I disagree that XML is more readable in general, but for the purpose of tagging blocks of text as <important>important</important> in freeform writing, JSON is basically useless
This seems like an actual good use for XML. Using it as a serialization format always rubbed me the wrong way (it’s super verbose, the named closing tag are unnecessary grammar-wise, the attribute-or-child question etc.) But to markup and structure LLM prompts and response it feels better than markdown (which doesn’t stream that well)
I think this article is 100% relevant to you today. Anthropic put out a training video, a number of months ago saying that XML should be highly encouraged for prompts. See https://m.youtube.com/watch?v=ysPbXH0LpIE
A very minor porcelain on some of the agent input UX could present this structure for you. Instead of a single chat window, have four: task, context, constraints, output format.
And while we're at it, instead of wall-of-text, I also feel like outputs could be structured at least into thinking and content, maybe other sections.
That first image, “Structure Prompts with XML”, just screams AI-written. The bullet lists don’t line up, the numbering starts at (2), random bolding. Why would anyone trust hallucinated documentation for prompting? At least with AI-generated software documentation, the context is the code itself, being regurgitated into bulleted english. But for instructions on using the LLM itself, it seems pretty lazy to not hand-type the preferred usage and human-learned tips.
There must be an OpenClaw YouTube video helping people post to hacker news, or something, because the front page is overrun with AI slop like this article, that makes no sense anyway. The author literally has no idea what any of this stuff means.
Sounds like as 1. XML is the cleanest/best quality training data (especially compared to PDF/HTML) 2. It follows that a user providing semantic tags in XML format can get best training alignment (hence best results). Shame they haven't quantified this assertion here.
HTML diverged from SGML pretty early on. Various standards over the years have attempted to specify it as an application of SGML but in practice almost nobody properly conformed to those standards. HTML5 gave up the pretence entirely.
Amazing how an entire profession that until yesterday would pride itself on precision, clarity (in thought and in writing), efficiency, and formality, has now descended into complete quackery.
Anthropic’s tool calling was exposed as XML tags at the beginning, before they introduced the JSON API. I expect they’re still templating those tool calls into XML before passing to the model’s context
Yeah like I remember prior to reasoning models, their guidance was to use <think> tags to give models space for reasoning prior to an answer (incidentally, also the reason I didn't quite understand the fuss with reasoning models at first). It's always been XML with Anthropic.
Exactly the same story here. I still use a tool that just asks them to use <think> instead of enabling native reasoning support, which has worked well back to Sonnet 3.0 (their first model with 'native' reasoning support was Sonnet 3.7)
I think the main advantage of the XML here is that the model is expected to have a matching end tag that is balanced, which reduces the likelihood of malformed outputs.
This has been the way for a long time, exploiting XML tags was a means of exfiltrating data or reversing a model for a while as well. Some platforms are still vulnerable to this.
> a contrast between Claude’s modern approach [...] XML, a technology dating back to 1998
Are we really at the point where some people see XML as a spooky old technology? The phrasing dotted around this article makes me feel that way. I find this quite strange.
XML has been "spooky old technology" for over a decade now. It's heyday was something like 2002.
Nobody dares advertise the XML capabilities of their product (which back then everybody did), nobody considers it either hot new thing (like back then) or mature - just obsolete enterprise shit.
It's about as popular now as J2EE, except to people that think "10 years ago" means 1999.
It's not the hot new thing but when has hype ever mattered for getting shit done? I don't think anyone who considers it obsolete has an informed opinion on the matter.
Typically a more primitive (sorry, minimal) format such as JSON is sufficient in which case there's no excuse to overcomplicate things. But sometimes JSON isn't sufficient and people start inventing half baked solutions such as JSON-LD for what is already a solved problem with a mature tech stack.
XSLT remains an elegant and underused solution. Guile even includes built in XML facilities named SXML.
XML is used a lot in standards and publishing industries -- JATS, EPUB, ODF, DOCX/XLSX/..., DocBook, etc. are all XML based/use XML.
Without being facetious, isn’t HTML a dialect of XML and very widely used?
HTML is actually a dialect of SGML. XHTML was an attempt to move to an XML-based foundation, but XML's strictness in parsing worked against it, and eventually folks just standardized how HTML parsers should interpret ill-formed HTML instead.
No, HTML was historically supposed to be a subset of SGML; XML is also an application of SGML. XHTML is the XML version of HTML. As of HTML5, HTML is no longer technically SGML or XML.
Also in finance. XBRL and FIXML although I do not know how widely used the latter is.
I kind of miss SOAP. Ahead of its time? Probably not, but I built some cool things on top of it
atproto's lexicon-based rpc is pretty soap-like
For me, even when it was first released, I considered obsolete enterprise shit. That view has not diminished as the sorry state of performance and security in that space has just reaffirmed that perception.
20 years old means 1980!
Obsolete enterprise shit I guess includes podcasting. Impressive for the enterprise.
I’d be very curious what lasting open formats JSON has been used to build.
didn't know html was spooky tech, TIL. /s
XML is still around, but I don't think many people would choose it as a serialization format today for something new.
The use of XML as a data serialization format was always a bad choice. It was designed as a document _markup_ language (it’s in the name), which is exactly the way it’s being used for Claude, and is actually a good use case.
It has a number of security issues which have not been fixed which could be used for really interesting exploitation.
If you think XML is old tech, wait until you hear of EDI, still powering Walmart and Amazon logistics. XML came in like a wrecking ball with its self-documenting promise designed to replace that cryptic pesky payload called EDI. XML promised to solve world hunger. It spawned SOAP, XML over RPC, DOM, DTD, the heyday was beautiful and Microsoft was leading the charge. C# was also right around this time. Consulting firms were bloomed charged with delivering the asynchronous revolution, the loosely coupled messaging promises of XML. I think it succeeded and it’s now quietly in the halls of warehouse having a beer or two with its older cousin the Electronic Data Interchange aka EDI.
EDI is XML now.
Yup. Kids these days...
The evidence suggests that XML was never that popular though for the general audience, you have to admit.
For Web markup, as an industry we tried XHTML (HTML that was strictly XML) for a while, and that didn't stick, and now we have HTML5 which is much more lenient as it doesn't even require closing tags in some cases.
For data exchange, people vastly prefer JSON as an exchange format for its simplicity, or protobuf and friends for their efficiency.
As a configuration format, it has been vastly overtaken by YAML, TOML, and INI, due to their content-forward syntax.
Having said all this I know there are some popular tools that use XML like ClickHouse, Apple's launchd, ROS, etc. but these are relatively niche compared to (e.g.) HTML
MS Office and Open-/LibreOffice are using zipped xml files (e.g. .docx, .xlsx and .odt). Svg vector graphics is xml, the x in ajax stands for xml (although replaced by json by now). SOAP (probably counts as the predecessor of REST) is xml-based.
XML was definitely popular in the "well used" sense. How popular it was in the "well liked" sense can maybe be up for debate, but it was the best tool for the job at the time for alot of use cases.
The thesis here seems to be that delimiters provide important context for Claude, and for that putpose we should use XML.
The article even references English's built-in delimiter, the quotation mark, which is reprented as a token for Claude, part of its training data.
So are we sure the lesson isn't simply to leverage delimiters, such as quotation marks, in prompts, period? The article doesn't identify any way in which XML is superior to quotation marks in scenarios requiring the type of disambiguation quotation marks provide.
Rather, the example XML tags shown seem to be serving as a shorthand for notating sections of the prompt ("treat this part of the prompt in this particular way"). That's useful, but seems to be addressing concerns that are separate from those contemplated by the author.
XML is a bit more special/first class to Claude because it uses XML for tool calling:
I'm sure Claude can handle any delimiter and pseudo markup you throw at it, but one benefit of XML delimiters over quotation marks is that you repeat the delimiter name at the end, which I'd imagine might help if its contents are long (it certainly helps humans).Except quotation marks look like regular text. I regularly use quotes in prompts for, ya know, quotes.
The GP isn't suggesting to literally use quotes as the delimiter when prompting LLMs. They're pointing out that we humans already use delimiters in our natural language (quotation marks to delimit quotes). They're suggesting that delimiters of any kind may be helpful in the context of LLM prompting, which to me makes intuitive sense. That Claude is using XML is merely a convention.
But should this extend to anything that could end up in Claudes context? Should we be using xml even in skills for instance, or commands, custom subagents etc.
And then do we end up over indexing on Claude and maybe this ends up hurting other models for those using multiple tools.
I just dislike how much of AI is people saying "do this thing for better results" with no definitive proof but alas it comes with the non determinism.
At least this one has the stamp of approval by Claude codes team itself.
The main benefit of using XML here seems to be that it forces clearer thinking and formulation from the user.
This matches my experience building AI-powered analysis tools. Structured output from LLMs is dramatically more reliable when you give the model clear delimiters to work with.
One thing I've found: even with XML tags, you still need to validate and parse defensively. Models will occasionally nest tags wrong, omit closing tags, or hallucinate new tag names. Having a fallback parser that extracts content even from malformed XML has saved me more than once.
The real win is that XML tags give you a natural way to do few-shot prompting with structure. You can show the model exactly what shape the output should take, and it follows remarkably well.
It'd be hilarious if XML schemas and validators were to make a comeback [1] to... interface with AI models.
[1] well of course XML is still heavily used in stuff like interfacing with automated wire transfers with big banks (at least in Europe) and all the digital payments directives etc. But XML is not widely used by the "cool" stuff.
Total tangent, but what vagary of HTML (or the Brave Browser, which I'm using here) causes words to be split in very odd places? The "inspect" devtools certainly didn't show anything unusual to me. (Edit: Chrome, MS Edge, and Firefox do the same thing. I also notice they're all links; wonder if that has something to do with it.)
https://i.imgur.com/HGa0i3m.png
CSS on the <a> tags:
word-break: break-all;
It's an error in the site's CSS. CSS has way better methods, like splitting words correctly depending on the language and hyphenating it.
Although I can never remember the correct incantation, should be easy for LLMs.
CSS word-break property
Ask Claude?
I am unconvinced.
To me it seems like handling symbols that start and end sequences that could contain further start and end symbols is a difficult case.
Humans can't do this very well either, we use visual aids such as indentation, synax hilighting or resort to just plain counting of levels.
Obviously it's easy to throw parameters and training at the problem, you can easily synthetically generate all the XML training data you want.
I can't help but think that training data should have a metadata token per content token. A way to encode the known information about each token that is not represented in the literal text.
Especially tagging tokens explicitly as fiction, code, code from a known working project, something generated by itself, something provided by the user.
While it might be fighting the bitter lesson, I think for explicitly structured data there should be benefits. I'd even go as far to suggest the metadata could handle nesting if it contained dimensions that performed rope operations to keep track of the depth.
If you had such a metadata stream per token there's also the possibility of fine tuning instruction models to only follow instructions with a 'said by user' metadata, and then at inference time filter out that particular metadata signal from all other inputs.
It seems like that would make prompt injection much harder.
Transformers look like perfect tech for keeping track of how deep and inside of what we are at the moment.
Transformers are able to recognize balanced brackets grammar at 97% success rate: https://openreview.net/pdf?id=kaILSVAspn
This is 3% or infinitely far away from the perfect tech.
The perfect tech is the stack.
Basically, the only way you're separting user input from model meta-input is using some kind of character that'll never show up in the output of either users or LLMs.
While technically possible, it'd be like a unicode conspiracy that had to quietly update everywhere without anyone being the wiser.
My intuition is it comes down to error-correcting codes. We're dealing with lossy systems that get off track, so including parity bits helps.
Ex: <message>...</message> helps keep track. Even better? <message78>...</message78>. That's ugly xml, but great for LLMs. Likewise, using standard ontologies for identifiers (ex: we'll do OCSF, AT&CK, & CIM for splunk/kusto in louie.ai), even if they're not formally XML.
For all these things... these intuitions need backing by evals in practice, and part of why I begrudgingly flipped from JSON to XML
I think XML is good to know for prompting (similar to how <think></think> was popular for outputs, you can do that for other sections). But I have had much better experience just writing JSON and using line breaks, colons, etc. to demarcate sections.
E.g. instead of
Just doing something like: Use case document processing/extraction (both with Haiku and OpenAI models), the latter example works much better than the XML.N of 1 anecdote anyway for one use case.
XML helps because it a) Lets you to describe structures b) Make a clear context-change which make it clear you are not "talking in XML" you are "talking about XML".
I assume you are right too, JSON is a less verbose format which allows you to express any structure you can express in XML, and should be as easy for AI to parse. Although that probably depends on the training data too.
I recently asked AI why .md files are so prevalent with agentic AI and the answer is ... because .md files also express structure, like headers and lists.
Again, depends on what the AI has been trained on.
I would go with JSON, or some version of it which would also allow comments.
Could you clarify, do those tags need to be tags which exist and we need to lear about them and how to use them? Or we can put inside them whatever we want and just by virtue of being tags, Claude understands them in a special way?
They probably don’t need to be specific values. The model is fine tuned to see the tags as signals and then interprets them
If it walks like a duck ... AI thinks it is something like a duck.
All the major foundation models will understand them implicitly, so it was popular to use <think>, but you could also use <reason> or <thinkhard> and the model would still go through the same process.
<ponderforamoment>HTML is a large subsection of their training data, so they're used to seeing a somewhat semantic worldview</ponderforamoment>
XML is much more readable than JSON, especially if your data has characters that are meaningful JSON syntax
I think readability is in the eye of the reader. JSON is less verbose, no ending tags everywhere, which I think makes it more readable than XML.
But I'd be happy to hear about studies that show evidence for XML being more readable, than JSON.
I disagree that XML is more readable in general, but for the purpose of tagging blocks of text as <important>important</important> in freeform writing, JSON is basically useless
This seems like an actual good use for XML. Using it as a serialization format always rubbed me the wrong way (it’s super verbose, the named closing tag are unnecessary grammar-wise, the attribute-or-child question etc.) But to markup and structure LLM prompts and response it feels better than markdown (which doesn’t stream that well)
I think this article is 100% relevant to you today. Anthropic put out a training video, a number of months ago saying that XML should be highly encouraged for prompts. See https://m.youtube.com/watch?v=ysPbXH0LpIE
A very minor porcelain on some of the agent input UX could present this structure for you. Instead of a single chat window, have four: task, context, constraints, output format.
And while we're at it, instead of wall-of-text, I also feel like outputs could be structured at least into thinking and content, maybe other sections.
That first image, “Structure Prompts with XML”, just screams AI-written. The bullet lists don’t line up, the numbering starts at (2), random bolding. Why would anyone trust hallucinated documentation for prompting? At least with AI-generated software documentation, the context is the code itself, being regurgitated into bulleted english. But for instructions on using the LLM itself, it seems pretty lazy to not hand-type the preferred usage and human-learned tips.
No, it’s two screenshots from Anthropic documentation, stitched together: https://platform.claude.com/docs/en/build-with-claude/prompt...
The post even links to that page, although there’s a typo in the link.
Author here: I have just fixed the typo. Thank you.
And yes, these are screenshots from Anthropic’s documentation.
They're not even stitched together ; there's just no padding between the two images.
It looks like a screenshot from the Claude desktop app, so I don't think the author is trying to disguise the AI origin of the marerial
You just hallucinated the content is AI generated.
"This is AI" is the new "This is 'shopped, I can tell by the pixels."
I can tell by the em dashes
There must be an OpenClaw YouTube video helping people post to hacker news, or something, because the front page is overrun with AI slop like this article, that makes no sense anyway. The author literally has no idea what any of this stuff means.
Wait am I in the minority talking to Claude in markdown? I just assumed everyone does that, or at least all developers. It seems to work really well.
I do that in openwebui for code indents like ```
Sounds like as 1. XML is the cleanest/best quality training data (especially compared to PDF/HTML) 2. It follows that a user providing semantic tags in XML format can get best training alignment (hence best results). Shame they haven't quantified this assertion here.
Makes sense
This isn’t surprising: XML’s core purpose was to simplify SGML for a wider breadth of applications on the web.
HTML also descended from SGML, and it’s hard to imagine a more deeply grooved structure in these models, given their training data.
So if you want to annotate text with semantics in a way models will understand…
XML and HTML are SGMLs
HTML diverged from SGML pretty early on. Various standards over the years have attempted to specify it as an application of SGML but in practice almost nobody properly conformed to those standards. HTML5 gave up the pretence entirely.
Amazing how an entire profession that until yesterday would pride itself on precision, clarity (in thought and in writing), efficiency, and formality, has now descended into complete quackery.
Are you talking about the office of the president of the united states?
This vague posting is kind dumb.
It's a simple observation. I'm not here to win internet points. I've never before seen so much cargo-culting and mystic belief among engineers.
Anthropic’s tool calling was exposed as XML tags at the beginning, before they introduced the JSON API. I expect they’re still templating those tool calls into XML before passing to the model’s context
Yeah like I remember prior to reasoning models, their guidance was to use <think> tags to give models space for reasoning prior to an answer (incidentally, also the reason I didn't quite understand the fuss with reasoning models at first). It's always been XML with Anthropic.
Exactly the same story here. I still use a tool that just asks them to use <think> instead of enabling native reasoning support, which has worked well back to Sonnet 3.0 (their first model with 'native' reasoning support was Sonnet 3.7)
Can you sniff it out with Wireshark?
They don't expose the raw context over the wire, it's all pre/post processed at their API endpoints.
I thought the goal was minimal instruction to let Claude determine the best way to solve the problem. Not adding this to my workflow anytime soon.
It is not for the end user, it is more for things like wrappers and automation scripts.
Nobody expects the end user to prompt the AI using a structured language like xml
How about other frontier models, and smaller models?
I think the main advantage of the XML here is that the model is expected to have a matching end tag that is balanced, which reduces the likelihood of malformed outputs.
This has been the way for a long time, exploiting XML tags was a means of exfiltrating data or reversing a model for a while as well. Some platforms are still vulnerable to this.
This sounds like something for harnesses, not end users. Are they really expecting us to format prompts as XML??
bemused by how competently designed this is, compared to enshittified blogs and whatnot
To be realistic, this design needs more weirdly sexual etsy garbage, “one weird tip,” and “punch the monkey”