How Prompt Prefix Order Determines Whether AI Cache Hits Ever Occur

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Prompt caching in AI systems works strictly on prefix matching — a provider can only reuse computed state up to the first token that differs between two requests. This means content that repeats but appears in a different position offers no cost savings whatsoever, making block ordering the single most critical factor in cache efficiency. Developers are advised to arrange prompt sections from most stable to least stable, placing system prompts and static references first, followed by session data, conversation history, and finally per-request content like retrieved documents. Common mistakes that silently break caching include injecting timestamps, unstable JSON serialisation, per-user data at the top of prompts, and unique request IDs added by middleware. Conversation history is cacheable despite growing each turn, because new messages are appended and the earlier prefix remains unchanged — but only if no volatile content is placed after it.

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Interpolation, GANs or Diffusion: Choosing the Right Image Upscaling Method

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Image dimensions, not file size, determine AI vision API token costs

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Solo Developer Builds Vinyl and Hi-Fi Community Platform Using AI Tools

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Why AI Image APIs Block Harmless Requests and How to Diagnose Refusals

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