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LLM Friendly: The structure is created to be very easily parsed by AI designs while reducing the token rely, saving costs and context window House.
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Cuando pides a un LLM que extraiga información y la devuelva en formato estructurado, TOON es suitable porque su sintaxis explícita ayuda al modelo a generar salidas correctamente formateadas desde el primer intento.
Optimizing info formats is often about guessing video games. "If I take out whitespace, the amount of do I help you save?" "What if I switch to YAML?"
A diferencia de JSON, que fue creado para intercambio de datos entre aplicaciones World-wide-web, TOON está optimizado para reducir el consumo de tokens, lo que se traduce en ahorros significativos tanto en costes de API como en capacidad de contexto.
Esta es la JSON2TOON característica más poderosa de TOON. Cuando tienes un array de objetos con la misma estructura (como listas de usuarios, productos, o transacciones), TOON convierte estos datos en un formato tabular donde las claves se definen una sola vez. Esto elimina la redundancia masiva que existe en JSON y puede reducir el tamaño hasta en un 70%.
Whether you are debugging a prompt, getting ready a dataset, or simply interested in the amount of dollars you could potentially conserve on tokens, the @toon-format/toon CLI is your new best friend.
TOON representa una evolución organic: un formato que reconoce que los LLMs son ahora consumidores primarios de datos estructurados, no solo intermediarios.
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TOON structure makes use of short vital abbreviations, eliminates pointless whitespace, and optimizes the JSON composition to reduce token depend even though preserving all information. This could certainly lessen token usage by thirty-60% when compared TOON to standard JSON.