Crawlers & control files
Machine-Readable Content
Content encoded so software can parse its meaning, not just display its text.
Machine-readable content is content structured and labelled so software can parse its meaning, not merely render it for a human. In practice that means clean semantic HTML, schema markup, clear headings, tables for comparisons, transcripts for media, and consistent naming — all of which tell a machine what each piece of content is.
Machine readability is foundational to AEO because engines retrieve and reassemble content; the clearer the structure, the more reliably they can extract the right passage and understand what it means. Two pages with identical prose can perform very differently in AI answers if one is marked up and structured and the other is a wall of unlabelled text.
The work is unglamorous but high-leverage: schema, semantic markup and consistent structure applied across a library compound into better extraction and citation everywhere.
Related terms
Structured Answer Block
A self-contained passage formatted so an engine can lift it whole as a direct answer.
Citation Readiness
How ready a page is to be quoted and attributed inside an AI answer.
Extractability
Whether a passage can be lifted into an AI answer and still stand on its own.
llms.txt
A proposed file that points AI models to the content you want them to read.
Fact Density
How many specific, sourced facts a passage packs per sentence.
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