#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
🇯🇵 Elezioni #Giappone – Clamoroso exit poll di TBS: Il Partito Liberal Democratico della premier #Takaichi potrebbe da solo ottenere per la prima volta nella storia la super maggioranza dei 2/3 per cambiare la Costituzione.
Governo: 356
#LDP (conservatori): 321
#Ishin (destra libertaria): 35
Opposizione: 109
#CRA (centro): 50
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#Sanseito (estrema destra): 11
#Mirai (democrazia digitale): 8
#JCP (comunisti): 3
Altri: 8
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