Create LLM-Readable Data: JSON, CSV, Feeds

LLM-readable data is content structured so large language models can parse it cleanly, trace facts, and cite sources with confidence. Use standardized formats (JSON, CSV, and content feeds), stable identifiers, consistent field names, and timestamps. Align records with entities, add machine-readable context, and keep files small and predictable. Doing this can increase visibility, accuracy, and citations across AI assistants.

What “LLM‑readable” really means (and why it matters)

LLMs read patterns. They do best with data that is tidy, consistent, and self-describing. LLM-readable data:

• Uses known formats (JSON/JSON‑LD, CSV, RSS/Atom/JSON Feed).
• Keeps schemas stable: predictable keys, types, and order.
• Includes context: entities, units, links, and dates in ISO 8601.
• Offers canonical URLs so models can cite a source.
• Ships in files/feeds sized for fast fetch and parsing.

When your data is easy to parse, models can pick it up, reason over it, and reference it. That boosts discoverability in AI answers, helps win more citations, and supports features like Google’s AI Overviews. Google also recommends JSON‑LD for structured data where possible, because it’s easier to implement at scale and validate for rich results.

Business impact: simple examples

• Product catalogs: A clean JSON feed with stable product IDs, price, stock, and canonical URLs helps LLMs answer shopping questions accurately and link back to you.
• Location pages: CSV with normalized addresses, geo coordinates, and business hours makes local Q&A reliable and can support AI Overviews with consistent facts.
• Knowledge articles: Articles with embedded JSON‑LD and a sitewide feed help assistants find updated guidance and attribute it.

Paired with entity-first content, this clarity forms a reliable “source of record” models can trust. If you’re building that source, study how to craft entity-first pages and use schema that helps LLMs.

The practical framework: JSON, CSV, and feeds

JSON (and JSON‑LD)

Use JSON for APIs and complex objects. JSON‑LD is ideal for web pages where you want structured data tied to a URL.

Core rules:

• Flat first, nested only when needed.
• Stable keys; avoid renaming.
• Types that don’t drift: string, number, boolean, array.
• ISO 8601 dates (e.g., 2025-09-09T12:34:56Z).
• Canonical URLs for each entity.

Example (compact product object):

json

{
  "id": "sku-12345",
  "name": "Wireless Headphones",
  "description": "Over-ear, 30h battery.",
  "brand": "Acme Audio",
  "category": "Headphones",
  "price": 129.99,
  "currency": "USD",
  "inStock": true,
  "updatedAt": "2025-09-09T10:00:00Z",
  "canonicalUrl": "https://example.com/products/wireless-headphones"
}

If you’re embedding on a page for search features, JSON‑LD is recommended and widely supported.

CSV (plus metadata)

CSV excels for tabular data and bulk exports. Make it tidy:

• Header row with stable column names.
• One type per column, one entity per row.
• UTF‑8 encoding, comma delimiter, quoted fields when needed.
• No merged cells, no empty header names.

Support validation and transformation with CSV on the Web (CSVW) metadata. CSVW adds a JSON metadata file to document columns, types, and constraints, and it can define how to convert CSV to JSON or RDF.

Example header:

id,name,category,price,currency,inStock,updatedAt,canonicalUrl  

Feeds (RSS/Atom/JSON Feed)

Feeds help models discover updates without crawling everything.

• Include stable GUIDs/IDs for items.
• Provide absolute, canonical links.
• Include updated/lastBuildDate.
• Keep descriptions concise and machine-friendly.
• Consider JSON Feed for simple, clean parsing.

Pair your feeds with answer-first content so LLMs can lift the right summary and citation. See the answer-first content pattern.

Quick-start checklist

• Choose format(s): JSON for APIs and objects, CSV for tables, feeds for updates.
• Define schema: IDs, required fields, types, allowed values.
• Normalize:
  • ISO 8601 times
  • Units and currencies
  • Slugs and categories
  • Consistent boolean flags (true/false)
• Add context:
  • canonicalUrl
  • entity references
  • description and shortSummary fields
• Optimize size:
  • 1–5 MB per file
  • paginate APIs; segment feeds
• Version and validate:
  • version field or header
  • JSON Schema or CSVW metadata
• Publish and monitor:
  • stable URLs/endpoints
  • freshness timestamps
  • access logs and error alerts

JSON vs CSV vs Feeds: what to use when

Use case	Best fit	Why
Complex entities (products, profiles, events)	JSON/JSON‑LD	Nested fields, typed data, easy embedding on pages
Bulk tabular exports, analytics	CSV (+ CSVW)	Compact, spreadsheet-friendly, schema validation via CSVW
Change notifications, content updates	RSS/Atom/JSON Feed	Lightweight discovery, easy polling, fast ingestion
Knowledge panels on web pages	JSON‑LD	Recommended for rich results and structured context

Common pitfalls and how to avoid them

• Inconsistent field names and types: Lock schemas early. Use JSON Schema or CSVW to guard against drift.
• Unlabeled units and currencies: Always include explicit units and a currency code per price.
• Missing canonical URLs: Add canonicalUrl on each record to help models cite your page.
• Oversized files: Split large exports by category/date. Paginate APIs. Compress where appropriate.
• No update signals: Include updatedAt per record and a lastBuildDate on feeds.
• Nested JSON too deep: Keep nesting shallow; provide reference IDs for related entities.
• Dirty CSV headers: Use simple, lowercase, hyphen_or_snake_case; avoid spaces and special characters.
• Locale-specific formats: Avoid commas as decimals, local date formats, or region-specific encodings.

How Neo Core builds LLM‑readable data

We align content, entities, and structure so models can parse and cite your site:

• Entity-first modeling: We map your products, services, locations, and people into clean, linked records. See how entity-first pages help models trust and reuse your data.
• Schema strategy: We implement the types and properties that matter most to LLMs and search features, following Google’s structured data guidance and practical schema that helps LLMs.
• Answer-first content: We pair structured data with scannable summaries to increase lift in AI experiences, based on the answer-first content pattern.
• Feed and API hygiene: We ship stable feeds with clear update signals and lean, validated JSON for ingestion.
• Measurement loop: We track citations, coverage, freshness, and error rates to guide iteration.

If you want to discuss your stack and timeline, you can easily contact our team.

Mini case example

A multi-location retailer wanted more AI attributions and better local answers. We:

1. Modeled each store as an entity with ID, address, hours, geo, and services.
2. Published a location CSV with CSVW metadata and per-store canonical URLs.
3. Embedded JSON‑LD on each location page with consistent properties and ISO 8601 hours.
4. Launched a JSON Feed for updates (new hours, closures).

Results over 90 days:

• Faster ingestion signals from assistants
• Fewer misattributed hours in AI answers
• More linked citations per branded and near-me queries

The playbook combined structured data, clean CSV, and steady feed freshness—small steps with outsized impact.

Advanced tips and trends

• CSVW for validation and transformation: Use metadata to enforce column types and map CSV into JSON/RDF when needed (W3C CSVW primer, W3C publications).
• JSON Schema for contracts: Validate your JSON at build time and in CI to catch data drift early.
• Entity linking at scale: Include internal IDs and canonical URLs; cross-link related entities in feeds so models “see” relationships.
• Lightweight summaries: Add shortSummary fields to records; keep them factual and human-readable. They’re easy for LLMs to lift.
• Plan for AI Overviews: Maintain consistent facts, timestamps, and provenance to support AI Overviews optimization.
• Earn citations: Cite-ready content, clear sources, and stable URLs can help you win more LLM citations.

Measurement: KPIs, tracking, and timelines

What to track:

• Coverage and validity:
  • JSON Schema pass rate
  • CSVW validation errors by column
  • Feed fetch errors and item count deltas
• Freshness:
  • Median lag from content update to feed exposure
  • Percentage of records updated in last 30 days
• Discoverability and citations:
  • Share of answers citing your URLs in assistants over time
  • Impressions/clicks where structured data is present (Search Console)
• Consistency:
  • Conflicting facts found across pages/feeds/APIs
  • 404/5xx rates on cited URLs

Typical timelines:

• Week 1–2: Audit entities, define schema, add JSON Schema/CSVW.
• Week 3–4: Implement JSON‑LD, publish feeds, and set up monitoring.
• Week 5–8: Stabilize contracts, fix validation errors, and grow coverage.
• Ongoing: Improve summaries, add entities, and prune stale data.

Why Partner with Neo Core

You get a practical stack: entity-first modeling, schema that serves both search and LLMs, tidy CSV exports with metadata, and clean JSON that validates every deploy. We pair structure with answer-first content so models can understand and cite your pages with less friction. Most teams see faster feed uptake and fewer data conflicts once the basics are in place.

If you’re ready to turn your site into a reliable source of record across AI experiences, reach out and we’ll shape a simple plan around your data and timelines. Start by sending a note through our contact page.

FAQs

• What is the fastest way to make my site more LLM-readable?
  • Start with JSON‑LD on key pages (products, locations, services), ensure ISO 8601 timestamps, and add canonicalUrl fields. Then publish a small, clean feed of recent changes to signal freshness.
• Should I pick JSON or CSV?
  • Use both where they fit. JSON/JSON‑LD is best for web pages and APIs with nested fields. CSV shines for tabular exports and bulk ingestion. CSVW metadata helps LLMs and tools interpret CSV reliably.
• How big should my files be?
  • Keep files small (1–5 MB) for fast fetches. Split large datasets by category/date. For APIs, paginate. For feeds, include only recent changes and link to canonical pages.
• Do I need JSON‑LD for everything?
  • Not everything. Use JSON‑LD on pages where search features and citations matter most. For backend systems and integrations, JSON or CSV is fine if it’s validated and consistent. Google supports JSON‑LD, Microdata, and RDFa, but recommends JSON‑LD for most use cases.
• How do I handle units and currencies?
  • Always include explicit units and a currency code per record. Document them in your schema and, for CSV, in CSVW metadata. Avoid assumptions based on locale.

Call to Action

If you want structured data, clean feeds, and entity-first content that LLMs can parse and cite, let’s make a plan tailored to your stack. Share your goals and current formats through our contact form. We’ll propose a lean, fast rollout that you can ship in weeks, not months.