#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
Not all carbon is the same - some heats the planet, others help #cool it.
1/. Black Carbon – Soot from incomplete combustion; strong warming effect
2/. Brown Carbon – Biomass smoke aerosols; absorbs sunlight
3/. Blue Carbon – Stored in coastal & marine ecosystems
4/. Green Carbon – Stored in forests, soils, and vegetation
5/. Red Carbon – Biological matter on snow & ice; accelerates melting
6/. Grey Carbon – Fossil fuel–based industrial emissions
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