Module 6 min
Structured Data
Schema.org markup turns prose into explicit, machine-parseable facts: Article, FAQ, HowTo, Organization, sameAs.
Why it mattersExplicit facts are easier to extract and trust than facts a model must infer.
Definition & Foundation
What it is, in plain words
Schema.org structured data turns prose into explicit, machine-parseable facts. Instead of hoping a model correctly infers that "starts at $29" is your price, JSON-LD states it unambiguously: "price": "29", "priceCurrency": "USD". The core types are few: Article, FAQPage, HowTo, Organization (with sameAs), and Product, and together they annotate most of what an engine needs to get right about you.
Schema is the one parseability layer engines demonstrably use; it has powered rich results for years and feeds the entity and fact extraction behind AI answers, unlike the still-unproven llms.txt and OKF. Two honest boundaries, though: schema does not manufacture authority (marking up a claim doesn't make it trusted), and it must never contradict your visible content; a mismatch between your JSON-LD and your page is a trust penalty, not a boost. Used well, it makes facts you already state explicit and consistent, so grounding has something clean to latch onto. If you searched "schema for geo", "structured data for geo", or "schema markup for geo", this is it: the same Schema.org work framed for AI answers, and "geo schema" is just the shorthand.
The Types That Earn Their Keep
Five JSON-LD types that annotate what engines need
Organization + sameAs
Declares who you are and links your identity across the web (sameAs to Wikidata, LinkedIn, Crunchbase, socials). This is the backbone of being a resolvable entity; pair it with Entity SEO so the model knows the "you" it's citing.
FAQPage
Marks up question-and-answer content as explicit pairs, a near-perfect match for how engines retrieve answers. It mirrors your visible answer-first structure machine-readably. Only mark up Q&A actually on the page.
HowTo
Structures step-by-step instructions so procedural answers extract cleanly, the schema companion to a numbered step list. Ideal for "how do I…" content where the sequence is the answer.
Article + author
Attaches author, dates, and publisher to your content, the machine-readable half of E-E-A-T for machines. Pair Article schema with a real Person/author entity so expertise is parseable, not just visible.
Product
States price, availability, and reviews as structured facts, increasingly consumed by shopping and comparison answers. Keep it exactly in sync with your visible pricing; a stale or contradictory Product schema actively misleads.
Myths vs Reality
Common misreadings, corrected
Putting It to Work
JSON-LD that mirrors your visible facts
The craft is adding accurate JSON-LD that restates what's already on the page, validating it, and keeping it in sync. The core steps are universal; the how-to differs by platform, so per-stack tabs follow the playbook.
The structured-data playbook
Prioritize the types that match your content
Organization + sameAs on your site-wide template first (it anchors your entity), then Article/author on content, FAQ and HowTo where you genuinely have Q&A and procedures, and Product on commerce pages. Don't add a type for content you don't have.
Prefer JSON-LD in the page head
Use <script type="application/ld+json"> blocks (Google's recommended format, and the cleanest for machines to parse) rather than inline microdata scattered through the HTML.
Mirror the visible content exactly
Every value in the JSON-LD must match what a human sees on the page: same price, same FAQ answers, same dates, same author. The schema is a machine-readable echo of the visible facts, never a place for extra or different claims.
Validate before and after shipping
Run pages through Google's Rich Results Test and Schema.org validator. Fix errors and warnings, and re-validate after template changes; a refactor that drops a schema block is a silent regression.
Keep it in sync on every content change
When pricing, authorship, or FAQ answers change, update the schema in the same commit. Wire dateModified to real updates so schema freshness matches page freshness; see Freshness & Cadence.
On your platform
product.liquid, but is often incomplete; audit it for price, availability, and review fields, and fix in the theme's JSON-LD block.theme.liquid so it renders site-wide; add FAQ schema on pages that actually have Q&A.dateModified and author to CMS fields so freshness and E-E-A-T signals update automatically when the item changes.<script type="application/ld+json"> in the layout: fully static, in the HTML at build time.<script type="application/ld+json"> blocks in the head; keep a shared partial for Organization/sameAs so it stays identical site-wide.Verification Checks
How to know it's really done
Your structured data works when it's accurate, validated, and in sync. Click a check to mark it verified:
Works Together With
The nodes this one leans on
Impact Weightage & Results TAT
What it moves, and how fast
Real but supporting leverage: schema is the parseability layer engines actually use, making your facts extractable and consistent. It amplifies content and entity work rather than earning citations on its own.
A long-standing classic-SEO asset; schema powers rich results and helps Google understand entities and content, paying into both scoreboards.
Editorial estimate of this node's contribution to your total GEO / SEO outcome. Nodes overlap, so weights don't sum to 100.
Tools for this
Go deeper
Always current
These links resolve live: what you get is generated or filtered the moment you click.
Fresh from the field feed refreshed July 8, 2026
- Jul 7 62% Of AI Brand Recommendations Vanish After One Buyer Question – New Clovion Data via @sejournal, @gregjarboe Search Engine Journal
- Jul 6 Why most original data never gets cited Growth Memo
- Jun 29 Why proprietary data is your most defensible AI citation asset Growth Memo
- Jun 25 Keeping Data-Driven Content Fresh Was a Monthly Slog. So We Taught an Agent to Do It. Ahrefs Blog