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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
Live: Get your virtual panda cuddles from Chongqing Zoo!
It's Saturday! Time for some super cute pandas. Yu Ai, Yu Ke, Mang Cancan, Qi Sanmei and Liang Yue in Chongqing Zoo get ready for clumsy rolls, silly play and fluffy cuteness. Join us to have a look! #panda
via CGTN
🩸🅰️🩸🩸🅰️
A Chinese zoo is under fire again for passing off dogs as pandas. This is the third time that people have been tricked by painting ordinary chow chows as pandas.
Visitors began to suspect that they weren't pandas when the spotted furry creatures started barking and panting like dogs.
The plan was perfect. What could go wrong?
#Panda#China
MARKHEMIST