# HSDS Schema Summary in Markdown for LLM Inference **URL:** \ **Category:** Technical\ **Tags:** datastructure, community\ **Created:** [March 21, 2025, 8:23pm UTC](https://forum.openreferral.org/t/hsds-schema-summary-in-markdown-for-llm-inference/627 "2025-03-21T20:23:25Z")\ **Posts on this page:** 1\ **Showing post:** 3
**Author:** ![CheetoBandito](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.openreferral.org/cheetobandito/32/151_2.png) [@CheetoBandito](https://forum.openreferral.org/u/CheetoBandito)\ **Post date:** [March 21, 2025, 10:42pm UTC](https://forum.openreferral.org/t/hsds-schema-summary-in-markdown-for-llm-inference/627/3 "2025-03-21T22:42:21Z")
I cannot be sure that CSV or JSON would perform differently, better or worse, when communicating knowledge to an LLM. I think in most cases previously I would just pass in regular text or maybe stuff I copy and paste from a webpage. I thought markdown was a nice balance of human readability with the ability to mimic the tabular format in a machine readable way that you find in the schema documentation. I do know that when embedding documents for vector inference, you have to chunk them into logical pieces. Markdown makes that easy because it has a defined way of capturing headers with the # symbol. When i embed documents for inference, I was finding it easy to chunk the documents based on those headers, and i figure engineers training models likely have similar pipelines using tools like Llama Parse. As with a lot of AI / ML stuff, it is great when you can pass in your question in a format similar to the the format it has seen a lot of when training the model… I bet YAML does a good job, because of the thousands of stack overflow posts… lol. But maybe markdown is more broadly usable and easier to keep organized on my computer 👨‍💻 --- _[View the full topic](https://forum.openreferral.org/t/hsds-schema-summary-in-markdown-for-llm-inference/627)._