使用結構化生成提升 Prompt 一致性與輸出評估
Original: Improving Prompt Consistency with Structured Generations
When developing applications based on large language models (LLMs) — such as AI agents, RAG systems, or automated workflows — one of the…
本文深入探討如何利用結構化生成（Structured Generations）解決 LLM 輸出格式不穩定的痛點。透過約束解碼（Constrained Decoding）技術（如 Outlines 或 TGI），能強迫模型輸出符合特定 JSON Schema 的內容。文章分析了這種技術的運作原理、如何進行評估，以及它對模型推理品質與生成速度的實際影響，是開發 Production-ready AI 應用的必讀指南。
When developing applications based on large language models (LLMs) — such as AI agents, RAG systems, or automated workflows — one of the biggest challenges developers face is the "unpredictability of outputs." Even when a prompt explicitly demands "return only JSON format," the model may still include extra explanatory text, omit brackets, or hallucinate, causing the backend parsing to fail.
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