#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
#REQ/USDT analysis :
#REQ is currently encountering resistance at the resistance zone. The price is likely to experience a rejection from this level and continue its bearish momentum. Previous low is expected to be tested. Wait for price retracement for entry.
TF : 15min
Entry : $0.0963
Target : $0.0928
SL : $0.0985