#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag
Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.
https://github.com/FareedKhan-dev/all-rag-techniques
⚡️ 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
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⚡️ 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
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"The Schnitzel Dilemma"
A short film about two colleagues planning their lunch date. Generated with runwayml #Gen3 new Act-One model 😍
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