#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
𝖺𝗏𝖺𝗍𝖺𝗋: 𝗍𝗁𝖾 𝗅𝖺𝗌𝗍 𝖺𝗂𝗋𝖻𝖾𝗇𝖽𝖾𝗋
>>> посмотрела ещё на выходных!! мне безумно понравился этот вариант экранизации ❤️
[ каст на мой взгляд идеальный 😌 ]
> пересматриваю сейчас изначальную 'легенду об аанге' и, да, она отличается, но тем не менее экранизация классная 🫶🏻
#avatar