#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
⭐️ 15 Passive income #Ideas
1 Sell on Amazon
2 Rent out a room
3 Create a gaming app
4 Start an ATM business
5 Buy a vending machine
6 Invest in royalty income
7 Invest in dividend stocks
8 Buy & hold growth stocks
9 Invest in rental properties
10 Automate a shopify store
11 Grow a Youtube chennel
12 Start an influencer IG account
13 promote products as an affiliate
14 Create your own affiliate program
15 Save more with a high yield savings
✨news forex & Cryptocurrency✅
y aquí mi trabajo honesto <3 espero que les guste . Hoy hice mi prueba de español y me fue piola. Si quieren sugerir algún tipo de post soy todo oídos. #ideas
También aceptamos ideas y sugerencias, cambios de color, todo a tu gusto, porque recuerda que se acerca el mes del amor 🥂❤️ y la amistad 🥰
(Fotos tomadas de internet)
#ideas