#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
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😄Nice
➖➖➖➖➖➖
🔘Nice has a positive meaning and can be similar to good, pleasant or enjoyable.
🔜 We had a really nice time with the monkeys at the zoo.
🔜 It was so nice to see you!
🔘 The negative is also common.
🔜 The weather isn't nice today. Take a coat with you.
🔘We use nice to describe people who are kind, polite or friendly.
🔜 He lent you his phone? What a nice guy.
🔜 I love seeing Susy. She's a really nice lady.
#Nice👨🏫@America
➖➖➖➖➖➖➖➖➖➖➖➖
🆕 Crypto News @Money
😁 Crypto Game @Egame
🇺🇸 US News @America
🇯🇵 Japan News @Japan
🇦🇪 UAE News @Dubai
▶️ Popular Movies @Videos
😜 Best Funny Video @Funnys