Stop wasting money on AI: 10 ways to cut token usage
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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.
Table of contents
Understanding AI tokensPrerequisitesSetting up the testing arenaOver 200k developers use LogRocket to create better digital experiencesUse the system instructions blockStop sequencesMedia resolution togglingCap or disable thinkingContext cachingUsing token-oriented object notation (TOON)Intelligent model routingSelective retentionDefine a response schemaPrompt optimizersConclusionSort: