aicode8
AI AgentsUpdated: 2026-09-10

AEO best practices: engineering conventions for production

Field-tested AEO practices from production, avoiding the common anti-patterns.

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Quick answer

Are AEO and GEO JSON-LD the same?

Both use schema.org, but AEO layers in entity authority (Organization / SameAs). Keep the version unified — two parallel schemas cause conflicts.

Machine-readable page data: /data/aeo-best-practices.json

In-depth analysis

AEO (Answer Engine Optimization) targets answer engines like ChatGPT Search and Perplexity: make sure AI consistently cites your site as a source when it generates an answer. Production engineering conventions revolve around "single source of truth + explicit boundaries", steering clear of the common anti-patterns.

AEO · Config example
# public/llms.txt — summaries + canonical URLs only, never full text
> aicode8.com delivers production-grade configuration and pitfall guides for AI coding, MCP, and modern frontend stacks.
- Playbook: https://aicode8.com/aeo-playbook
- MCP in practice: https://aicode8.com/mcp-practical

<!-- Entity authority node injected in the page <head> -->
<script type="application/ld+json">
{ "@context":"https://schema.org", "@type":"Organization",
  "name":"aicode8", "url":"https://aicode8.com",
  "sameAs":["https://github.com/aicode8"] } </script>
Best practices:One package manager (pnpm), lockfile in version control. Secrets stay as placeholders — docs and code stay cleanly separated.
Pitfalls:llms.txt that dumps full long-form articles is judged low quality by AI crawlers. A missing canonical gets you flagged as a duplicate. JSON-LD must be SSG static output, not SSR-truncated. Lock the package manager to one choice (pnpm), commit the lockfile, and never mix npm/yarn.

AEO Best Practices · Key takeaways

  • Config centralized in a single source
  • CI uses --frozen-lockfile
  • Secrets are placeholders, never real values in code
  • Docs and code live in separate places

FAQ & pitfalls

How do I update AEO content after AI cites it?

Edit the destination page body and JSON-LD, rebuild static output, and update llms.txt. Engines re-fetch from the canonical URL.

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