#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
Curated Crypto | ꘜ
🤑WTF: ETH options open interest just hit a 1.5-year high!
Degens are loading up on longs, while whales are stacking ETH at all-time record pace!
But hedge funds are going mega short - CME ETH futures short positioning just keeps smashing new records!
Green But Red in real life!
#WTF
The suspect attempted to escape an interrogation room by breaking through the wall while no one was watching—only to be caught shortly after.
@Viral_Today / #wtf
Imagine a coffee table that moves around the house by itself—well, you don’t have to anymore because it’s real. It’s just as fascinating as it is creepy.
@Viral_Today / #wtf