Unverified50% confidenceFactExact time
解决大模型知识边界问题的两大技术方向是持续微调（Fine-tuning）和RAG（检索增强生成），LangChain主要服务于RAG
1
Sources
50%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
LangChain Fundamentals: A Deep Dive into Components, Chains, and Agents
bilibili大模型零基础入门_7/8/2026
Related Claims
UnverifiedThe industry best practice is to combine fine-tuning with RAG—first fine-tuning to give the model domain understanding capability, then supplementing the latest dynamic knowledge through RAG.82% 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.77% similarVerified检索增强生成（RAG）通过基于真实知识库内容回答来解决大模型的幻觉问题76% similarUnverifiedCompared to baking knowledge into model parameters, RAG supports real-time knowledge base updates and is the mainstream solution for enterprise-level AI applications to address the problem of knowledge staleness.76% similarUnverifiedThe developer solved the problem of large models being unfamiliar with FoxTable syntax through a dual strategy of a knowledge base (RAG approach) combined with system prompts.76% similar
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