#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
#GRT/USDT analysis :
#GRT has broken out of its trendline with strong momentum following a bounce back from the support zone. The price is expected to maintain its bullish momentum and test previous highs. The current level presents a favorable entry opportunity.
TF : 1W
Entry : $0.2023
Target : $0.3446 and $0.4902
SL : $0.1450
#GRT/USDT analysis :
#GRT is currently in a downtrend, making lower lows (LLs) and lower highs (LHs), and is trading below the 200 EMA. The price is facing rejection from both the 200 EMA and the resistance zone. It is expected to decline from this point and potentially test the previous swing low.
TF : 1D
Entry : $0.1682
Target : $0.1294
SL : $0.1940
💰#GRT has a falling wedge pattern on 8H Time frame, we expec it will pump alot in the case of breakout, waiting now..⌛️
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️
💰#GRT bounced back from the Support zone on 12H Time frame, we are waiting for breakup and pullback to the broken trendline 💫
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️