Do LLMs understand TOON format without special instructions?

Yes, modern LLMs understand TOON format natively without requiring special instructions or prompting, thanks to their training on diverse text data.

LLMs like GPT-5, Claude, and Gemini were trained on massive datasets including YAML files, configuration files, indented text structures, Python dictionaries, and various human-readable data formats - all of which share structural similarities with TOON.

The models learn to recognize hierarchy through indentation, key-value relationships through colons, and list structures through consistent patterns. When you provide data in TOON format, the LLM intuitively understands the structure just as it would with JSON, because the semantic information (what data represents) remains identical - only the syntax differs.

No instruction overhead:

You don't need to explain TOON format in your system prompt, saving additional tokens. Simply provide the data and ask questions or give instructions about it.

Consistent parsing:

LLMs reliably extract values, navigate nested structures, and reference specific fields from TOON data.

Bidirectional support:

LLMs can both read TOON format in input and generate TOON format in output if requested.

Edge cases to consider:

• Very deeply nested structures (5+ levels) might occasionally confuse the model
• Data with unusual formatting or inconsistent indentation could cause parsing issues
• When mixing multiple data formats in one prompt, being explicit about format boundaries helps

For critical production applications, include a validation step to ensure the LLM correctly interpreted the structure, especially when dealing with complex nested data.