Structuring JSON for LLMs to Optimize Token Usage
This article discusses best practices for structuring JSON data when building LLM-powered applications to avoid token waste and improve model performance.
Why it matters
Optimizing JSON structure is crucial for building cost-effective and accurate LLM-powered applications, as it can reduce token consumption and improve model performance.
Key Points
- 1LLMs tokenize JSON character-by-character, leading to inefficient token usage
- 2Flatten nested JSON structures to reduce the number of tokens required
- 3Remove unnecessary fields from JSON payloads to further optimize token usage
- 4Consider using TOON (Token-Oriented Object Notation) for large JSON payloads
Details
The article explains that when developers use raw API responses as-is in LLM prompts, the models struggle to understand the nested JSON structure, leading to token waste and potential misinterpretations. To address this, the author recommends three key strategies: 1) Flatten nested JSON structures to a single level of depth, 2) Strip out any fields that the LLM doesn't need, and 3) Use TOON (a compact alternative to JSON) for large payloads over 500 tokens. These techniques can significantly reduce the token usage and improve the reliability of LLM-powered applications.
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