#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
⚡️ Advanced Camera Control is now available for #Gen3 Alpha Turbo. Choose both the direction and intensity of how you move through your scenes for even more intention in every shot
VideoGenerator | SUBSCRIBE
⚡️ Camera controls have appeared in Runway #Gen3 alpha!
No official announcement yet, but it looks like they’re rolling it out gradually. 😍
Credits: Pierrick Chevallier
VideoGenerator | SUBSCRIBE
"The Schnitzel Dilemma"
A short film about two colleagues planning their lunch date. Generated with runwayml #Gen3 new Act-One model 😍
VideoGenerator | SUBSCRIBE