#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
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Mathematicians found that many formulas used to calculate π are connected.
About 43% of known π formulas come from one main form. Another 51% belong to related groups.
This shows many π formulas are not separate. They share common structures in math.
π is still an infinite number with no repeating digits.
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[Read more]
@googlefactss
#math#pi#mathematics#science#facts