Unverified50% confidenceFactExact time
Traditional machine learning engineering focuses on model training, feature engineering, and algorithm optimization, while modern LLM application development revolves around API calls, prompt engineering, retrieval-augmented generation (RAG), and agent orchestration.
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9/12/2026
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UnverifiedIntroducing LLMs means that data science teams need to master skill stacks including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and model evaluation.74% similarVerifiedTraining strategies for mathematical LLMs include continued pre-training on large-scale mathematical corpora on top of general pre-training, followed by supervised fine-tuning with chain-of-thought reasoning data, combined with reinforcement learning using process reward models.72% similarVerifiedModern LLMs have acquired capabilities for planning, reasoning, and tool use through instruction tuning and reinforcement learning from human feedback (RLHF).72% similarUnverifiedFine-tuning is suitable for teaching a model a specific output style or domain tone, while RAG achieves immediate-update-ready results through an external knowledge base, and the two are often used in combination in engineering.71% similarUnverified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70% similar
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