The grammar of AI visibility. Discover how MRS (Machine-Ready Structure) structures data to maximize semantic assimilation and minimize token cost.
This document is not the official specification (available at chkcd.com), but a guide on how Kat3x uses the MRS format in its Knowledge Seeding experiments.
MRS is a markup format designed to be parsed by Large Language Models (LLMs) without preprocessing. Unlike JSON, which is optimized for classical APIs, MRS uses explicit semantic sections (@claims, @entities, @limitations) that drastically reduce the cognitive (and token) cost for the model during RAG processes.
Each object begins by declaring the data schema once in the header, separating keys with a pipe (|). Values follow aligned below.
company{name|sector|core_product}:
name: Kat3x Observatory
sector: AI Research
core_product: Semantic AnalyticsAnnotations with the at-symbol guide the LLM's attention to specific metadata or contexts, essential for the correct routing of information.
@entity: research_report @id: KAT3X-001 @context: machine_readability