#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag
Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.
https://github.com/FareedKhan-dev/all-rag-techniques
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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