#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
🫣Windows 11 2001-yilda qanday ko'rinishga ega bo'lardi
📅 Agar Microsfot Windows 11 ni 22 yil oldin ishlab chiqqanida taxminan uning dizayni shu ko'rinishda bo'lardi.Bu siz uchun hunuk ko'rinishi mumkin ammo ushbu dizayn 2001-yil uchun supper!
Reaksiyani mazasini qochiramiz 🔥
🌐Birinchi raqamli Windows Blog | #windows11
🫣Windows 11 2001-yilda qanday ko'rinishga ega bo'lardi
📆 Agar Microsfot Windows 11 ni 22 yil oldin ishlab chiqqanida taxminan uning dizayni shu ko'rinishda bo'lardi.Bu siz uchun hunuk ko'rinishi mumkin ammo ushbu dizayn 2001-yil uchun supper!
👉🏼Birinchi raqamli Windows Blog | #windows11
🫣Windows 11 2001-yilda qanday ko'rinishga ega bo'lardi
📆 Agar Microsfot Windows 11 ni 22 yil oldin ishlab chiqqanida taxminan uning dizayni shu ko'rinishda bo'lardi.Bu siz uchun hunuk ko'rinishi mumkin ammo ushbu dizayn 2001-yil uchun supper!
👉🏼Birinchi raqamli Windows Blog | #windows11