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Technical AI Infrastructure

Before any content strategy matters, machines have to be able to fetch, parse and identify your site. This track is the infrastructure layer: crawler permissions, plain-text mirrors, structured data emitted from templates, and the public entity record that makes your schema resolve to a real organisation.

4 articles ~21 min total Ordered — each part builds on the last
After this track
  • Write robots.txt rules that admit search crawlers and opt out of training separately
  • Ship llms.txt and a full-text bundle generated from real content, so they cannot drift
  • Emit a JSON-LD entity graph where author and organisation actually resolve
The reading path Reading order is the dependency order
  1. 1
    Optimizing Robots.txt for AI Agents: GPTBot, Perplexity & Claude Start here GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended: what each AI user agent does and the exact robots.txt policy that fits your citation goals.
    5 min read
  2. 2
    The Complete Guide to the llms.txt Standard for AI Model Indexing What the llms.txt standard is, the exact Markdown format it requires, who has adopted it, the skeptic case — and a decision framework for whether to ship one.
    5 min read
  3. 3
    Integrating Dynamic JSON-LD Schemas into Hugo Static Templates Generate Article, FAQPage, and Organization JSON-LD from Hugo front matter with Go template partials — build-time schema with jsonify escaping and zero plugins.
    5 min read
  4. 4
    Constructing Wikidata Q-ID Anchors: Step-by-Step Guide for AI Entity Authority How to earn a Wikidata Q-ID for your brand the right way: notability rules, the exact properties to set (P31, P856, P571), references, and deletion traps.
    6 min read
After this track