El endpoint de detección de IA devuelve un objeto JSON que contiene los resultados del análisis del texto enviado. Esto incluye probabilidades que indican la posibilidad de que el texto sea generado por IA o sea original.

Los campos clave en los datos de respuesta incluyen "all_tokens" (total de tokens analizados), "used_tokens" (tokens utilizados en el análisis), "real_probability" (probabilidad de que el texto sea original) y "fake_probability" (probabilidad de que el texto sea generado por IA).

Los datos de respuesta están estructurados en un formato JSON, con pares clave-valor que proporcionan información sobre el análisis del texto. Esto permite una fácil interpretación e integración en aplicaciones.

El parámetro principal para el punto de acceso de detección de IA es el texto que se va a analizar. Los usuarios pueden personalizar sus solicitudes al proporcionar diferentes entradas de texto para evaluar diversos contenidos.

La precisión de los datos se mantiene a través del uso de la avanzada tecnología de OpenAI, que se refina continuamente. Actualizaciones regulares y controles de calidad aseguran que los algoritmos de detección permanezcan efectivos y confiables.

Los casos de uso típicos incluyen la detección de plagio en trabajos académicos, la verificación de ensayos de estudiantes para comprobar su originalidad, la garantía de calidad del contenido para creadores y la validación de contenido generado por usuarios en chatbots.

Los usuarios pueden utilizar los datos devueltos al interpretar los valores de "real_probability" y "fake_probability" para evaluar la originalidad del texto. Esta información puede guiar decisiones sobre la autenticidad del contenido y posibles revisiones.

Los usuarios pueden esperar patrones de respuesta donde los valores de "real_probability" y "fake_probability" sumen 1.0, lo que indica una clara distinción entre el contenido original y el generado por IA, lo que facilita un análisis sencillo.

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