#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
#FUN/USDT analysis :
#FUN has established a breakout from its trendline after rebounding from the support zone. The price is anticipated to continue rising from its current level and is likely to test the previous swing high.
TF : 4H
Entry : $0.003263
Target : $0.003644
SL : $0.003008
#FUN/USDT analysis :
#FUN is currently consolidating within a resistance zone near the 200 EMA. A price rejection is expected from there, and a continuation of the downtrend is anticipated. Wait for the price to break below the trendline for a short entry.
TF : 2H
Entry : $0.00314
Target : $0.00287
SL : $0.00326