What is the difference between JSON Schema and "Function Calling" for AI consistency?

JSON schemas and function calling serve similar purposes with different mechanisms. Function calling defines parameters as schema-like structures, guiding the LLM to generate tool arguments. JSON schemas constrain entire response structure in structured output mode. Function calling integrates with tool execution pipelines automatically. Schemas are more flexible for arbitrary output structures not tied to function calls. Function calling provides implicit context about parameter purpose through names and descriptions. Schemas require explicit definitions of all fields. Function calling works for single tool invocations. Schemas handle complex multi-entity responses better. Both enforce type safety and required fields. Function calling has better model fine-tuning for reliability. Schemas offer more sophisticated validation like pattern matching and dependencies. Choose function calling for tool-based workflows and action execution. Use schemas for complex data extraction or document generation. Validate outputs with our JSON Editor at jsonconsole.com/json-editor regardless of approach. Many applications benefit from combining both: function calls with schema-validated responses.