Unverified50% confidenceTradeoffExact time
There are two technical approaches to vertical-domain knowledge injection: one is injecting domain knowledge at the foundation model level through fine-tuning, which requires large amounts of annotated data and offers low flexibility; the other is keeping the foundation model unchanged and dynamically injecting knowledge at inference time through tool calls, RAG, or structured prompts, which offers higher flexibility but places greater demands on prompt engineering.
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9/11/2026
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UnverifiedVertical domain models often outperform general large language models on specific tasks because they can be fine-tuned with reinforcement learning and instruction tuning targeted at concrete workflows74% similarUnverified大语言模型的叙事垂直化优化通常涉及微调(Fine-tuning)和检索增强生成(RAG)两条核心技术路径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% similarUnverifiedTargeted specialized programming models may outperform larger general-purpose flagship models in vertical scenarios.70% similar
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