📄 中文摘要
在将 GPT-4 的概念验证版本推向生产环境时，工程团队面临选择：是微调模型还是构建检索管道。两种方法都旨在提升大型语言模型（LLM）在特定领域的实用性，但其实现方式、成本结构和故障模式截然不同。选择错误的方法不仅会浪费 GPU 预算，还可能导致生产系统脆弱、维护成本高昂以及调试困难。该框架提供了实用的决策依据，帮助团队在微调与检索增强生成之间做出明智选择。
微调与检索增强生成：何时使用这两种生产 LLM 方法
出处: Fine-tuning vs RAG: When to Use Each Approach for Production LLMs
发布: 2026年3月4日
📄 English Summary
Fine-tuning vs RAG: When to Use Each Approach for Production LLMs
When transitioning a proof-of-concept with GPT-4 into production, engineering teams face the critical decision of whether to fine-tune the model or build a retrieval pipeline. Both approaches aim to enhance the utility of large language models (LLMs) for specific domains, yet they operate in fundamentally different ways, have distinct cost profiles, and fail in unique manners. Choosing the wrong method can lead to wasted GPU resources and create a brittle production system that is costly to maintain and difficult to debug. A practical decision framework is provided to assist teams in making an informed choice between fine-tuning and retrieval-augmented generation.