#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
#CKB/USDT analysis :
#CKB has retested the previously respected zone. The price is anticipated to rebound from the current level and test higher levels.
TF : 1W
Entry : $0.006300
Target : $0.020440
SL : $0.004500
#CKB/USDT analysis :
#CKB has experienced a breakout and has successfully retested the previous breakout zone. It is expected to rise from the current level and test previous highs. A bullish momentum is anticipated in the near future.
TF : 1D
Entry : $0.01408
Target : $0.01916
SL : $0.01239