Why TOON is Replacing JSON in LLM Workflows (and Saving You 50% on Tokens) скачать в хорошем качестве

Why TOON is Replacing JSON in LLM Workflows (and Saving You 50% on Tokens)

JSON (JavaScript Object Notation) has been the universal champion of data exchange for a decade, but for developers working with Large Language Models (LLMs), its verbosity is costing a fortune in API fees. Every brace, quote, and comma is a token—and tokens cost money! In this in-depth video, we break down the crucial differences:The Design Rationale: How TOON's tabular, indentation-based structure is optimized for LLMs, while JSON relies on explicit delimiters $(\{\}, [], :)$ for universal compatibility.Efficiency Gains: We show real-world benchmarks where TOON drastically cuts token consumption and, surprisingly, often improves LLM output accuracy thanks to explicit schema declarations.Use Cases & Trade-offs: When should you always stick with JSON (deeply nested or irregular data, traditional APIs)? And when does TOON shine brightest (uniform arrays, database query results, RAG pipelines)? The future of data is not either JSON or TOON, but knowing when to use each strategically. TOON is poised to become the standard for GenAI communication, making agentic workflows faster, cheaper, and more reliable. Watch to learn how to implement TOON today and maximize your LLM budget. #TOONvsJSON #TOONFormat #TokenEfficiency #LLMDevelopment #JSON #StructuredData #DataFormat #BackendDevelopment #APIDesign #AIEra #PromptEngineering #AIAgents #GenerativeAI #GenAI #AICommunity #TechTrends #Coding #Programming #SoftwareEngineering