Unverified50% confidenceOpinionExact time
The article divides large model deployment into four stages: native large model, prompt engineering, RAG, and Agent, corresponding respectively to generation, expression, knowledge, and action.
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50%
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9/26/2026
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Unverified大模型开发学习可拆分为五个递进阶段：大模型基础、RAG知识库开发、Agent智能体开发、模型微调进阶、项目实战79% similarUnverifiedThe article argues that large model development learning is divided into four layers: foundational cognition, API invocation, prompt engineering, and four core paths of RAG/fine-tuning/Agent/multimodal.78% similarUnverifiedLarge model application development can be divided into three progressive levels: prompt engineering, RAG retrieval-augmented generation, and model fine-tuning.78% similarUnverifiedThe paper argues that assertions about hosted model behavior must be explicitly annotated along four dimensions: the model identifier tested, the service period, the measurement tool, and the inference configuration.74% similarUnverifiedPrompt engineering and RAG address making large models answer questions well, while Agent addresses making large models proactively think and solve problems.70% similar
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