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.

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.

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.

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.

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…

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

How about other frontier models, and smaller models?

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.

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.