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I have been building multiple LLM systems and for our Organization biggest cost savings weren't from prompt-wordsmithing or model switchings. Sharing useful to anyone watching their token bill : 1) JSON → TOON for structured output: JSON was not made for LLMs. well you can implement your own verison that fits for your needs that reduce tokens usage but what worked for us was TOON. TOON cut output our tokens by ~30% same information, way less syntax tax. 2) Full markdown/HTML → condensed markdown

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Article URL: https://github.com/dvcoolarun/taste-ai Comments URL: https://news.ycombinator.com/item?id=48608707 Points: 1 # Comments: 0

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Many projects and addons exists to reduce the verbosity on standard bash / git /npm etc. Commands that agents pass regularly as tools to LLMs. (eg. rtk, headroom, lean-ctx). Tool output compression does yield good token savings. Though it can lead to increased turns - effectively nullifying the per turn token savings. This is the current topology. Are we going to see a class of products and libraries that structure o/p to what models want to see? Comments URL: https://news.ycombinator.com/item?i

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