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docs: add note on upcoming retrieval accuracy benchmarks

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Johann Schopplich
2025-10-27 07:59:41 +01:00
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README.md
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@@ -4,6 +4,9 @@
**Token-Oriented Object Notation** is a compact, human-readable format designed for passing structured data to Large Language Models with significantly reduced token usage. **Token-Oriented Object Notation** is a compact, human-readable format designed for passing structured data to Large Language Models with significantly reduced token usage.
In other words, if YAML and CSV had a baby, optimized for LLM contexts.
TOON borrows YAML's indentation-based structure for nested objects and CSV's tabular format for uniform data rows, then optimizes both for token efficiency in LLM contexts.
> [!TIP] > [!TIP]
> Wrap your JSON in `encode()` before sending it to LLMs and save ~1/2 of the token cost for structured data! > Wrap your JSON in `encode()` before sending it to LLMs and save ~1/2 of the token cost for structured data!
@@ -28,6 +31,9 @@ users[2]{id,name,role}:
2,Bob,user 2,Bob,user
``` ```
> [!NOTE]
> I built TOON to save tokens when sending large datasets to LLMs at work, where I tend to have uniform arrays of objects that benefit from the tabular format.
## Key Features ## Key Features
- 💸 **Token-efficient:** typically 30–60% fewer tokens than JSON - 💸 **Token-efficient:** typically 30–60% fewer tokens than JSON
@@ -38,6 +44,9 @@ users[2]{id,name,role}:
## Token Benchmarks ## Token Benchmarks
> [!NOTE]
> Benchmarks for LLM accuracy and retrieval are currently in development.
<!-- automd:file src="./docs/benchmarks.md" --> <!-- automd:file src="./docs/benchmarks.md" -->
| Example | JSON | TOON | Tokens Saved | Reduction | | Example | JSON | TOON | Tokens Saved | Reduction |