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On top of that, JSON lacks created-in schema enforcement, forcing builders to explain knowledge structures in just prompts, which further more consumes tokens and improves the probability of LLMs producing non-compliant output. Its syntactic rigidity indicates a single misplaced comma or quotation can invalidate an entire doc, requiring strong error handling. LLM tokenizers usually split punctuation into various tokens, exacerbating the fee issue.
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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 data formats is usually about guessing online games. "If I take out whitespace, just how much do I help save?" "Imagine if I swap to YAML?"
A diferencia de JSON, que fue creado para intercambio de datos entre aplicaciones Net, TOON está optimizado para reducir el consumo de tokens, lo que se traduce en ahorros JSON TO TOON significativos tanto en costes de API como en capacidad de contexto.
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Combina TOON con técnicas de prompt engineering. En lugar de incluir todos los datos, incluye un resumen en texto y los detalles completos en TOON solo cuando el LLM los necesite.
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The TOON CLI gets rid of the guesswork Together with the --stats flag. When encoding, this feature calculates the estimated token TOON depend and demonstrates you the discounts quickly. That is invaluable when you're budgeting for top-volume LLM calls.
The actual electrical power of the TOON CLI arrives when you chain it with other instruments like curl and jq. You are able to fetch facts from an API, filter it down to the Necessities, and convert it to TOON in only one line—willing to be pasted right into a prompt or sent to an inference endpoint.