Learn 10 practical ways to reduce token usage in LLM apps using system instructions, stop sequences, caching, TOON, and more.

LogRocket is a frontend monitoring platform that helps developers debug and troubleshoot issues in web applications by providing session replay, error tracking, and performance monitoring capabilities. With LogRocket, developers can gain insights into user interactions, identify bugs, and optimize application performance to deliver better user experiences. Developers can learn how to integrate LogRocket into their web applications, analyze user sessions, and diagnose frontend issues effectively.

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Practical guide to reducing LLM token usage and costs in AI-powered applications. Covers 10 techniques including: using system instructions instead of embedding persona in user prompts, stop sequences to halt unnecessary output, lowering image media resolution for OCR/classification tasks, configuring thinking budgets for simple queries, context caching for RAG applications, TOON (a compact JSON subset for AI), LLM routing to match model capability to task complexity, selective retention via vector DB for conversation history, structured response schemas, and prompt compression with LLMLingua. Code examples use the Google Gemini SDK in JavaScript.

Stop wasting money on AI: 10 ways to cut token usage

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Using token-oriented object notation (TOON)