OP here.

I took the unofficial IKEA US dataset (originally scraped by jeffreyszhou) and converted all 30,511 products into a flat, markdown-like protocol called CommerceTXT.

The goal: See if a flatter structure is more efficient for LLM context windows.

The results: - Size: 30k products across 632 categories. - Efficiency: The text version uses ~24% fewer tokens (3.6M saved total) compared to the equivalent minified JSON. - Structure: Files are organized in folders (e.g. /products/category/), which helps with testing hierarchical retrieval routers.

The link goes to the dataset on Hugging Face which has the full benchmarks.

Parser code is here: https://github.com/commercetxt/commercetxt

Happy to answer questions about the conversion logic!

I don't understand why new proposed standards are still polluting the root namespace (also see llms.txt).

These things should be put under /.well-known [1], not in the root.

[1] https://en.wikipedia.org/wiki/Well-known_URI

There’s already a schema.org spec that defines a JSON-LD structured data that you can embed on every of your product page to provide a machine readable interface of your product.

For example, Google’s indexers already use this to surface pricing data. https://developers.google.com/search/docs/appearance/structu...

Interesting. I had been thinking recently about grep-friendly structured text file formats given the constraints of regex. But I hadn't considered that you could design a structured text file format to be LLM-friendly given token constraints.

I've heard that LLMs can perform worse with these more efficient representations compared to e.g. JSON, because they've seen far fewer examples of them during training. Do you know how true that is?

years ago i did a small tool that, when you entered a product number, would scan all IKEA-websites with currency Euro and return the prices for each of them ; not that i expected furniture tourism to become a thing but it was funny

Interesting! So did you do any experiments on a relevant subset of the data to test whether LLM performance degrades by introducing a new, presumably unknown to the LLM, format?

"OP here" is the funniest tell that shows up when using an LLM to write a post for HN or Reddit.

It's funny because it makes zero sense in the body of an initial post!

In comments replying to people downthread - maybe. But opening a top-level post with "Original Poster here" is just silly and shows a lack of respect for community etiquette.

https://hn.algolia.com/?dateRange=pastYear&page=0&prefix=tru...

Any practical use for this IKEA data specifically?

Or just a handy open data set you could use to prove out the concept?