Save LLM Tokens: TOON Instead of JSON for Working with Neural Networks

When you work with large language models (LLMs), every token has a cost, especially if you regularly pass structured data as JSON.

How can you save tokens when using AI in business?

Instead of JSON, use more compact data formats such as TOON, which do not repeat field names in every record. This matters most when you feed large arrays of structured data into LLM pipelines, where every token has a cost.

Why is JSON inefficient for working with LLMs and neural networks?

In JSON, field names are repeated in every record of an array, wasting tokens. An array of 100 records with the fields id, name, email, status repeats those names 100 times instead of once.

What is TOON and how does it save tokens?

TOON is a compact format for structured data in which field names are stated once instead of being repeated in every record, as they are in JSON. This addresses the problem of token consumption in LLM pipelines.