Migrate your prompts to new models and optimize them on Amazon Bedrock
https://aws.amazon.com/blogs/machine-learning/migrate-your-prompts-to-new-models-and-optimize-them-on-amazon-bedrock/📌 【Amazon Bedrock 新功能】告別手動調優：一次任務即可完成跨模型 Prompt 優化與遷移
TL;DR：Amazon Bedrock 推出 Advanced Prompt Optimization，透過強化學習機制，協助工程師快速將 Prompt 遷移至新模型並提升效能。
🎣 Prompt 遷移與優化：生成式 AI 開發的效能瓶頸
當你開發出一個表現穩定、使用者滿意的生成式 AI 應用程式時，挑戰才剛開始。當 Amazon Bedrock 上出現了更快、更便宜且能力更強的新模型時，你該遷移嗎？這並非簡單的決策。
🤔 重複且耗時的開發循環
目前，將 Prompt 遷移至新模型或針對現有模型進行優化，仍是建構生成式 AI 應用中最耗費人力的環節。開發者通常會陷入以下循環： 改寫 Prompt → 執行測試案例 → 比較結果 → 微調 → 重複上述步驟。
當專案規模擴大，需要同時處理生產環境中的所有 Prompt 範本，並對多個候選模型進行評估時，這項工作量會隨著開發雄心而呈指數級成長。
🧩 Amazon Bedrock Advanced Prompt Optimization：以指標驅動的自動化工作流
為了打破這個瓶頸，Amazon Bedrock 推出了 Advanced Prompt Optimization。這項工具的核心理念是「從評估指標出發」，並透過類似強化學習（reinforcement learning）的迴圈機制運作，而非僅僅依賴直覺式的改寫。
其技術特點包括：
- 跨模型優化：單一任務即可針對 Amazon Bedrock 上的最多 5 個模型進行優化。
- 對照測試：在優化過程中，系統會同時比較原始 Prompt 與優化後 Prompt 的效能表現。
- 實務導向：允許開發者引導 Prompt 與回應的變化方向，確保優化過程與實際的使用案例及數據緊密結合。
🎯 實務啟示
對於需要頻繁更換模型或追求極致效能的工程師來說，這項工具將原本需要數天甚至數週的手動迭代，轉化為以數據為依據的引導式工作流，大幅縮短了生成式 AI 應用程式的開發週期。
🔗 來源
- 標題：Migrate your prompts to new models and optimize them on Amazon Bedrock
- 作者／機構：Jesse Manders @ AWS ML
- 連結：https://aws.amazon.com/blogs/machine-learning/migrate-your-prompts-to-new-models-and-optimize-them-on-amazon-bedrock/
#AmazonBedrock #AWS #GenerativeAI #PromptEngineering #LLM #MachineLearning #AIOptimization #CloudComputing #SoftwareEngineering #PromptOptimization
原始資料 AWS ML · 收集於 2026-07-31
摘要原文
Migrating prompts to new models on Amazon Bedrock, or optimizing them for your current model, is still one of the most manual parts of building a generative AI application. Say you have built a deployed generative AI application. It works. Your prompts are tuned, your outputs are consistent, and your users are happy. Then a new model becomes available on Amazon Bedrock that is faster, cheaper, and more capable, and you must decide whether to migrate. The decision should be straightforward, but it rarely is. Customers spend days to weeks optimizing prompts and re-evaluating responses when they migrate to a new model. The same effort applies when they try to improve performance on their current model. The cycle looks the same every time. Rewrite the prompt, run it against test cases, compare results, tweak, and repeat. Now multiply that work by every prompt template in production and every model candidate worth evaluating. The result is a problem that scales with your ambition. Today, Amazon Bedrock introduces Advanced Prompt Optimization , a tool that optimizes prompts for up to 5 models on Bedrock while comparing original and optimized performance. In this post, we show you how to use Amazon Bedrock Advanced Prompt Optimization to migrate and optimize prompts across multiple models in a single job. This approach replaces days of manual iteration with a guided, metrics-driven workflow. Prompt migration and optimization sit at a critical chokepoint in the generative AI development lifecycle. When this step is slow or manual, you feel it in 4 areas: You need a prompt optimizer with built-in evaluations that test multiple models at once. It should also let you guide how prompts and responses change, grounding the optimization in real use cases and data. Amazon Bedrock Advanced Prompt Optimization works backward from your evaluation metric. Instead of rewriting prompts heuristically, the system operates in a reinforcement learning-style feedback loop, without changing mo
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