Your product scraper runs perfectly for months. Every field validates. Types match. Formats are correct. Then someone in analytics notices the "product weight" field now contains shipping estimates. Or "price" occasionally includes promotional text like "starting from $99." The data structure is fine. The meaning has drifted.

Rule-based validation catches obvious structural problems. Semantic validation catches the subtle meaning shifts that preserve structure but break data quality.

How Meaning Drifts Without Breaking

Websites don't just break—they evolve. A field label changes from "weight" to "net volume" to nothing at all. The same information appears in different formats across regional variants. Content moves around during A/B tests. Your scraper keeps extracting data from the right location, passing all structural checks, but the meaning has shifted.

When you're running agents across multiple e-commerce sites, this pattern appears everywhere. One site shows prices as "$120," another as "USD 120.00," a third as "starting from $99." Rules would need separate patterns for each variation. Semantic validation understands they're all expressing price information, even when formats differ.

Product descriptions reveal another common case. Your scraper extracts text from the right location, the right length, passes all checks. But the website's A/B test moved content around, and you're now capturing shipping policies instead of product details. Rules can't catch this because text is text. The structure is intact.

In our infrastructure work running agents across thousands of sites, we've learned that semantic drift is more common than structural breaks. Websites evolve constantly—content moves during A/B tests, field meanings shift during redesigns, regional variations express the same concept differently. At scale, these quiet changes create more data quality problems than obvious structural failures. Rule-based validation can't catch them because the structure looks fine.

The Operational Trigger

Teams typically add semantic validation after experiencing a specific kind of failure: the silent drift. Your scraper keeps running, your validation keeps passing, but dataset quality degrades over time. You discover the problem weeks later when downstream reports look wrong or customers complain about bad data.

Another trigger: cross-site consistency. When you're scraping similar data from multiple sources—product catalogs, pricing information, inventory status—the same semantic concept appears in wildly different formats. Traditional validation requires separate rules for each site. LLMs can understand that "in stock," "available now," and "ships within 24 hours" all indicate availability, even though they're structurally different.

What Semantic Validation Catches

• Contextual anomalies surface when data is technically correct but contextually wrong. A product price of $0.01 might pass all validation rules but is almost certainly an error. Semantic validation can flag it as anomalous based on context—other products in the category, historical pricing patterns, what makes sense for this type of item.

• Semantic drift happens when meaning changes without structure changing. Your scraper extracts a "category" field that used to contain "Electronics > Laptops" but now contains "Shop All Electronics." Both are strings, both pass validation, but one is a navigation breadcrumb and the other is a marketing message.

• Cross-field consistency problems appear when individual fields look fine but their combination doesn't make sense. A laptop listed as weighing 50 pounds, or a phone with a 20-inch screen. Rules can check individual field constraints, but semantic validation understands relationships between fields.

• Format variations multiply when the same information appears in different formats that rules struggle to normalize. Prices as "$120," "USD 120.00," "120 dollars," or "starting at $99"—semantic validation can extract the actual price value from all these variations without explicit rules for each.

How Teams Layer These Approaches

Semantic validation runs alongside rule-based validation, not instead of it. Rules catch the obvious structural problems cheaply and quickly. Semantic checks catch the subtle meaning problems that rules miss.

A typical implementation validates data in layers. First pass: rule-based checks for types, formats, required fields. Second pass: semantic analysis for contextual anomalies, meaning drift, cross-field consistency. This approach keeps costs manageable while catching both structural and semantic issues.

Recent implementations combine Pydantic models with LLM extraction—using Pydantic to define expected structure and LLMs to ensure extracted data matches that structure semantically. The LLM doesn't just extract data; it validates that what it extracted makes sense given the schema definition.

What teams actually experience: less time updating rules, more time building. When a website changes, semantic validation adapts without requiring rule updates. When new edge cases appear, contextual understanding handles them without explicit programming. The system becomes more resilient to the web's natural evolution.

When This Approach Makes Sense

Semantic validation fits specific operational contexts:

High-maintenance environments where websites change frequently—when you're maintaining scrapers across 200 sites and 30 redesign quarterly, rule maintenance becomes unsustainable. Semantic validation means those redesigns don't require immediate rule updates.

Semantic complexity where meaning matters more than structure—product descriptions, classification tasks, anything requiring contextual understanding. When you need to distinguish between product features and shipping policies in the same text block, semantic validation understands the difference.

Cross-site consistency when extracting similar data from many sources—semantic validation understands that "in stock," "available now," and "ships within 24 hours" all indicate availability, even though they're structurally different across sites.

Quality-critical applications where downstream systems or business decisions depend on data accuracy. The cost of bad data exceeds the cost of thorough validation.

Semantic validation isn't free. It's slower than rule-based checks and costs scale with validation volume. Teams need to decide which data deserves semantic validation versus simple structural checks. The typical pattern: use semantic validation for high-value data where meaning matters most. Product descriptions, pricing information, inventory status—data that feeds directly into business decisions or customer-facing systems.

For teams building web agent infrastructure at scale, semantic validation represents an evolution in how we think about data quality. Not about replacing rules, but adding contextual understanding where rules reach their limits. The web changes constantly. Validation needs to adapt with it.