#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
In which language does "39" (spoken as san kyuu) sound like "thank you" due to the influence of English?
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In which language is "520" commonly used in messaging to mean "I love you"?
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How many English words made their first recorded appearance in Shakespeare’s writings?
A) 200
B) 1700
C) 4000
D) 18000
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The Week in Numbers: Christmas is cancelled
Chinese coal prices are at record levels, U.S. gasoline is at seven-year highs and benchmark crude climbs by the day. Signs of an energy crunch can be found wherever you look. More of the big numbers in business from the past week.
#News#Reuters#numbers#energycrisis#France#Bitcoin
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