Key Engineering Takeaways
- LLMs prioritize content structured with clean semantic headers, explicit definitions, and answer-first summaries.
- Publishing a standardized /llms.txt file gives AI crawlers direct access to core entity summaries and authoritative markdown links.
- Entity grounding via JSON-LD schema increases direct brand citation probability in AI search engines.
Design passages that can survive extraction
Answer engines retrieve passages, compare sources, and synthesize a response. A page must still earn ordinary crawlability and relevance, but its most useful claims also need to remain clear when removed from the surrounding design.
The extraction-ready page pattern
- Answer first: state the definition, comparison, number, or recommendation before adding explanation.
- Keep the subject explicit: avoid paragraphs full of pronouns whose meaning disappears outside the page.
- Separate evidence from opinion: attach dates, units, methods, and primary sources to factual claims.
- Ground the entity: keep author, organization, product, and topical schema consistent with visible content.
- Make discovery easy: use canonical URLs, stable HTML, internal links, sitemaps, and optional machine-readable summaries.
A compact answer block
answer-span.html
<h2>What is citation share of voice?</h2>
<p>Citation share of voice is the percentage of tracked AI answers
that cite a brand or domain for a defined prompt set and time window.</p>Optimize the evidence chain
There is no guaranteed citation switch. Improve the odds by making the claim easy to retrieve, easy to understand, and easy to verify against consistent first- and third-party evidence.
Core Disciplines
Answer Engine Optimization (AEO)Generative Engine Optimization (GEO)llms.txt StandardSchema GroundingLLM Prompt Tracking