#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
Huge thanks to Cointelegraph for having us on the Chain Reaction AMA 🎙️
In this conversation, Managing Partner of DWF Labs, Andrei Grachev, shares more insights on the #GENIUSAct.
Regulation brings clarity. Clarity brings confidence. And confidence brings capital.
This is the turning point for institutional crypto.
Read more here.