Can a strict JSON schema prevent AI "hallucinations" in structured data?

JSON schemas prevent structural hallucinations but cannot eliminate content hallucinations entirely. Schemas guarantee valid data types, required fields, and format compliance, preventing fabricated field names or invalid structures. However, LLMs can still generate factually incorrect content within valid schema constraints. For example, a schema ensures "age" is a number, but cannot verify if that number is accurate. Schemas prevent adding non-existent fields the model invents. Enum constraints limit categorical hallucinations by allowing only predefined values. Pattern matching reduces format hallucinations for emails, dates, and URLs. Required fields prevent omission hallucinations where critical data is missing. Schemas work best combined with other techniques: RAG for factual grounding, few-shot examples for format, and validation against source data. Use our JSON Validator at jsonconsole.com/json-editor to verify schema-compliant outputs before processing. Schemas significantly improve reliability but are one layer of defense, not complete solution. Combine schemas with fact-checking for production systems.