#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
CSS yordamida dizayn mahoratingizni oshiring!
Saytingiz ko‘rinishini yanada chiroyli, zamonaviy va professional qilish uchun quyidagi foydali vositalardan foydalanib ko‘ring:
1️⃣Border Radius Generator – burchaklarni yumaloq qilishda qulay:
🔗10015.io/tools/css-border-radius-generator
2️⃣Glassmorphism Generator – shaffoflik effekti bilan zamonaviy dizaynlar yarating:
🔗hype4.academy/tools/glassmorphism-generator
3️⃣Shadows Generator – realistik soyalar yaratish uchun ajoyib vosita:
🔗shadows.brumm.af
4️⃣Box Shadow Examples – tayyor soya effektlarini tanlab olib qo‘llang:
🔗getcssscan.com/css-box-shadow-examples
#CSS
💻@dasturlash_hayoti— dasturchilar hayoti va dasturlash olami haqida qiziqarli loyiha!
#css
🖥 Ko‘rsatilgan gradiyentlarga asosan CSS kod chiqarib beruvchi foydali sayt.
LINK👉neumorphism.io
💻@dasturlash_hayoti— dasturchilar va dasturlash hayotini yoritib boradigan loyiha!