If you've ever deployed structured data only to watch Google Search Console throw validation errors your way, you know how frustrating it can be to debug JSON-LD after the fact. The good news? You can catch these issues before they ship using Fraseβ€”a tool originally built for content optimization but increasingly useful as a structured data validation ally.

Why Structured Data Validation Matters in 2026

Google's rich results requirements get stricter every year, and small mistakes in your schema markup can mean the difference between a featured snippet and invisibility. Missing required properties like "price" on a Product schema or malformed "datePublished" fields on Article markup will get your structured data flagged. Frase helps you audit these proactively rather than reactively.

The Five-Step Workflow

The approach is straightforward: write AI prompts inside Frase that analyze your JSON-LD against Google's specific requirements for each schema type. First, export or paste your raw schema markup into a Frase document. Then craft a prompt that instructs the AI to cross-reference your data against Google's documentationβ€”checking for required properties, proper formatting, and common pitfalls like duplicate IDs or incorrect value types.

Writing Effective Audit Prompts

Your prompts need specificity to be useful. Instead of asking "is this valid?", ask Frase to identify missing required fields per Google's schema requirements, flag any property values that don't match expected formats (like ISO 8601 dates), and suggest corrections with explanations. The goal is actionable output you can act on immediately.

Catching Common Issues Before Publishing

This method shines at catching the tedious errors humans miss: a missing "image" property on Recipe schema, an incorrect "priceCurrency" format, or nested Organization data that contradicts your BreadcrumbList. Run this audit as part of your pre-publish checklist and you'll spend less time in Google's rich results test tool.

Key Takeaways

  • Frase's AI capabilities can audit JSON-LD against Google's schema requirements proactively
  • Write specific prompts that check for required properties, formatting issues, and common mistakes
  • Integrate this validation step into your pre-publish workflow to catch errors early
  • This approach works across multiple schema types: Products, Articles, FAQs, Events, and more

The Bottom Line

Structured data validation shouldn't be an afterthought. Using Frase for AI-assisted audits is a practical way to level up your SEO quality assurance processβ€”catching issues before they impact your search visibility.