Transformers, Large Language Models, and Agentic RAG

Transformers, Large Language Models, and Agentic RAG

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Transformers have revolutionized the field of Natural Language Processing (NLP) with their remarkable ability to handle sequential data. Among the various variants of transformers, Large Language Models (LLMs) have demonstrated impressive language understanding capabilities. However, the recent development of Agentic RAG (Reality Augmented Generator) has sparked interest in the potential applications of these models in diverse domains. This video delves into the fundamental differences between transformers, LLMs, and Agentic RAG, exploring their architectures, strengths, and limitations.

The advent of Agentic RAG has opened up new avenues for creative coding and innovative problem-solving. By combining the capabilities of transformers and LLMs, Agentic RAG has the potential to transform the way we approach complex tasks. For instance, it can be used to generate realistic and immersive virtual environments, revolutionizing fields such as gaming, education, and entertainment.

To further reinforce the study of transformers, LLMs, and Agentic RAG, we suggest exploring resources such as research papers, online courses, and open-source libraries. Some notable resources include the Transformer model architecture, the transformer library in TensorFlow, and the Scholarly article on Agentic RAG by researcher [Name]. Additionally, participating in online forums and discussion groups, such as Reddit's r/MachineLearning and r/NLP, can provide a platform to engage with experts and enthusiasts in the field.


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#NLP #Transformers #LLMs #AgenticRAG #AIResearch #MachineLearning #STEM

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