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**Token-Oriented Object Notation** is a compact, human-readable format for serializing JSON data in LLM prompts. It represents the same objects, arrays, and primitives as JSON, but in a syntax that minimizes tokens and makes structure easy for models to follow.
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**Token-Oriented Object Notation** is a compact, human-readable encoding of the JSON data model for LLM prompts. It provides a lossless serialization of the same objects, arrays, and primitives as JSON, but in a syntax that minimizes tokens and makes structure easy for models to follow.
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TOON combines YAML's indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays. TOON's sweet spot is uniform arrays of objects (multiple fields per row, same structure across items), achieving CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient.
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@@ -25,16 +25,15 @@ Think of it as a translation layer: use JSON programmatically, and encode it as
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-[Key Features](#key-features)
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-[When Not to Use TOON](#when-not-to-use-toon)
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-[Benchmarks](#benchmarks)
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-[Playgrounds](#playgrounds)
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-[📋 Full Specification](https://github.com/toon-format/spec/blob/main/SPEC.md)
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