AI search engines extract structured signals, not raw text. Without JSON-LD, a crawler guesses your page type, author, and topic from prose — which makes citations inconsistent and attribution wrong. With the right markup it takes 10 minutes to fix.
Three Schema.org types cover most developer pages:
Organization — who publishes this. Required for brand attribution in AI-generated answers.
Article with datePublished/dateModified — marks content as citable with a freshness signal AI engines weigh heavily.
FAQPage or HowTo — the highest-value signal: Q&A markup maps directly to how AI assistants answer questions, and these blocks get quoted disproportionately.
Minimal JSON-LD for a blog or docs page
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"name": "Your Brand",
"url": "https://example.com"
},
{
"@type": "Article",
"headline": "Your page title",
"author": { "@type": "Organization", "name": "Your Brand" },
"datePublished": "2026-01-01",
"dateModified": "2026-07-11"
}
]
}
</script>
For Q&A content, append a FAQPage block — each question + answer pair becomes a separately citable passage.
Check all five JSON-LD signals in one call
curl -X POST https://citeready-api.sprytools.com/v1/audit \
-H "content-type: application/json" \
-d '{"url":"https://yoursite.com"}'
The structured_data category reports jsonld_present, jsonld_valid, jsonld_org_or_website, jsonld_content_type, and jsonld_faq_howto — pass/warn/fail with the exact fix to apply for each.
const res = await fetch('https://citeready-api.sprytools.com/v1/audit', {
method: 'POST',
headers: { 'content-type': 'application/json' },
body: JSON.stringify({ url: 'https://yoursite.com' }),
});
const { categories } = await res.json();
const sd = categories.find(c => c.id === 'structured_data');
console.log(sd.score, sd.checks);
Free at https://citeready.sprytools.com — 3 checks/day, no signup.
Which Schema.org type were you missing when you first ran the audit?
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