#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
#Murmuration#PKM 这个有意思,[《中国的地图每张都是错的》](https://telegra.ph/%E6%AF%8F%E5%BC%A0%E4%B8%AD%E5%9B%BD%E5%9C%B0%E5%9B%BE%E9%83%BD%E6%98%AF%E9%94%99%E7%9A%84%E8%BF%98%E6%98%AF%E6%95%85%E6%84%8F%E7%9A%84-05-15),这是翻译版本。原版在这里 [《Every map of China is wrong》](https://medium.com/@anastasia.bizyayeva/every-map-of-china-is-wrong-bc2bce145db2) 。🤷我说怎么地图啥都飘呢,哈哈哈