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首先,In production, nearly every request to a deployed LLM carries the same system prompt — the instructions that define the model’s behavior. Under naive allocation, each of those requests stores its own full copy of the system prompt’s KV cache. With 10 concurrent requests and a 200-token system prompt, that is 10 identical copies of the same data occupying separate memory regions.
其次,This is foundational but insufficient. Yes, use the best model you can afford. Yes, craft your prompts carefully with examples and constraints. But don't fool yourself into thinking that a great prompt is enough. I've seen too many teams ship "GPT-4 with a really good system prompt" and call it enterprise-ready.,更多细节参见搜狗输入法方言语音识别全攻略:22种方言输入无障碍
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最后,Undeveloped internal versions and evaluation data The source offers an unusual glimpse into Anthropic's planned model updates and the difficulties of cutting-edge development.
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