#jupyter_notebook
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
#RAY/USDT analysis :
#RAY is currently consolidating within a flag pattern, indicating a potential continuation of its upward trend. A breakout above the $5.890 level could signal a move to higher price levels. Consider entering a long position upon confirmation of the breakout.
TF : 1D
Entry : $5.890
Target : $9.045
SL : $4.000
🔥$RAY
Entered this position as a speculative play within the SOLANA ecosystem. Monitor pullbacks carefully, manage entries with discipline, and always conduct your own due diligence before participating.
X: https://x.com/i/communities/2033099708164378981
Website: https://x.com/bbcworld/status/2033099192877429170?s=46
🔗 Contract: https://solscan.io/token/G7ydggVFm4RVTTsd3E8MWh7angBs78MTmZN3gVZPpump
https://dexscreener.com/solana/HAVxMaLK96qgiyRdvX4YVpjfajU49zWot4tdjxPesMrg
#RAY#SOLANA#TESLACALLS