Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines
This technical article addresses the inefficiency of using JSON for feeding structured data into Large Language Models (LLMs), a practice referred to as paying a 'JSON tax.' The author introduces TOON, a serialization format designed to reduce token consumption by eliminating repetitive field names in structured records. Unlike JSON, which repeats keys for every object, TOON declares fields once and streams values in a compact tabular form, making it ideal for prompts containing repeated records like support tickets or CRM entries. The piece emphasizes that TOON is lossless and should not replace JSON in backend systems or APIs. Instead, developers should convert JSON to TOON specifically when preparing data for LLM input. The article outlines scenarios where TOON provides significant value, such as handling large datasets with uniform structures, while noting that benefits diminish for deeply nested, irregular, or very small data structures. It serves as a guide for developers looking to optimize LLM pipeline costs and performance by adopting more token-efficient data serialization methods without altering existing infrastructure.
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Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines
This technical article addresses the inefficiency of using JSON for feeding structured data into Large Language Models (LLMs), a practice referred to as paying a 'JSON tax.' The author introduces TOON, a serialization format designed to reduce token consumption by eliminating repetitive field names in structured records. Unlike JSON, which repeats keys for every object, TOON declares fields once and streams values in a compact tabular form, making it ideal for prompts containing repeated records like support tickets or CRM entries. The piece emphasizes that TOON is lossless and should not replace JSON in backend systems or APIs. Instead, developers should convert JSON to TOON specifically when preparing data for LLM input. The article outlines scenarios where TOON provides significant value, such as handling large datasets with uniform structures, while noting that benefits diminish for deeply nested, irregular, or very small data structures. It serves as a guide for developers looking to optimize LLM pipeline costs and performance by adopting more token-efficient data serialization methods without altering existing infrastructure.
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