Structured Data AI Formatter (JSON-LD)
Paste raw AI-generated text and convert it to valid JSON-LD structured data using Product, FAQ, or Recipe schema buttons. Extract key-value pairs and get SEO-ready markup.
Structured Data AI Formatter
How to generate structured data for AI
Paste your raw product details, FAQ pairs, or recipe text into the input field. Click Product, FAQ, or Recipe to generate valid JSON-LD schema markup that search engines and AI models can parse. The output is ready to embed in your HTML β copy it with one click.
For example, pasting "Blue Wireless Headphones, Premium sound quality with 30-hour battery, $129.99" and clicking Product generates a complete schema.org Product object with name, description, price, and availability fields. The FAQ button detects Q:/A: patterns and produces a valid FAQPage schema. The Recipe button extracts ingredients from bullet points and numbered steps.
Convert messy product descriptions, FAQ text, or recipe notes into structured JSON-LD that Google, ChatGPT, and other AI systems can parse without losing structure or introducing encoding errors.
Generate valid JSON-LD markup for Product, FAQ, and Recipe schemas that Google's Rich Snippet guidelines require. The copy button wraps output in script tags ready to paste into your HTML head.
Review AI-generated product pages or recipe content by seeing how the structured data extractor interprets the text β if fields are missing or misaligned, the source content needs revision.
Structured data formatting pipeline
The tool applies a three-stage pipeline to transform raw data into AI-optimised text.
Input parsing
Raw data is parsed into an internal representation regardless of source format. JSON is parsed natively. CSV is split into headers and rows. Key-value pairs are normalised into a consistent schema.
Optimisation
The parsed data is formatted according to the selected style. Table format uses aligned columns with headers. List format uses hierarchical bullet points. JSON format produces minified or pretty-printed output based on token budget.
Token budgeting
The tool estimates token count using the chars/4 approximation and warns if the formatted output exceeds typical context windows. Compact formatting options reduce token usage by 20-40% compared to raw data.
Model-specific adaptation
Output is adapted for the selected model's preferences. Claude receives XML-tagged sections. ChatGPT receives system/user role markers. Gemini receives markdown-structured sections. Llama receives plain text with clear delimiters.
Frequently asked questions
What data formats are supported?
JSON (objects and arrays), CSV with headers, and plain key-value pairs separated by colons or equals signs. The parser is lenient with formatting and handles missing quotes, trailing commas, and inconsistent whitespace.
How does formatting affect model performance?
Well-structured input reduces hallucination because the model can clearly distinguish between data fields. Consistent formatting also improves extraction accuracy when you ask the model to parse results back into structured data.
Can I use this with custom API pipelines?
Yes. The JSON output mode produces a clean, parseable format that can be embedded directly into API request payloads. The formatted text output works with any LLM provider's text input endpoint.
Does compression lose information?
No. The formatting changes presentation, not content. All values are preserved exactly. Compression reduces whitespace, abbreviates headers, and removes redundant formatting while maintaining data fidelity.