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Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
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#WIF/USDT Analysis-
After breaking out, the price retested the breakout level and is now taking support around it. It has the potential to reach a new all-time high.
T.F.- 1-D
ENTRY- at CMP
SL- 2.89
TARGET- 4.82
Note: If the stop-loss is triggered before entry, disregard the trade as the price action may develop differently.