#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
Lookonchain | ꘜ
Whales are accumulating $BGB recently.
0x8900 withdrew 192,668 $BGB($936K) from #Bitget over the past 2 months.
0x171D withdrew 30,607 $BGB($134K) from #Bitget 2 days ago.
0x7C9C withdrew 20,980 $BGB($102K) from #Bitget over the past 3 months.
Notably, #Bitget has burned a total of 860M $BGB($5.25B) over the past 8 months, reducing the total supply by 43%.
https://intel.arkm.com/explorer/address/0x89006C3aADfF87c5113b835660E3459C6Ad61F16
https://intel.arkm.com/explorer/address/0x171D1285a9a8De3f16d4c45706d4E2F4A5C9e175
https://intel.arkm.com/explorer/address/0x7C9C4f9046ba2173fae539FE62eEFAb1aBAD1523