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Large Language Models (LLMs):
- LLMs like GPT (Generative Pre-trained Transformer) are deep learning models trained on vast amounts of text data to understand and generate human-like text.
- They use Transformer architecture, which excels in tasks like language understanding, translation, summarization, and more.
- LLMs are based on unsupervised learning and can generate coherent text given a prompt, relying on learned patterns in the data.
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Retrieval-Augmented Generation (RAG):
- RAG combines the strengths of LLMs with information retrieval.
- Instead of generating responses purely based on learned patterns, RAG retrieves relevant information from a large database or knowledge repository using a retrieval mechanism.
- This retrieved information is then used by the LLM to generate more accurate, informative, and contextually relevant responses.
- RAG aims to improve the factual accuracy and relevance of LLM-generated content by incorporating external knowledge during the generation process.
In essence, while LLMs generate text based on learned patterns, RAG enhances this process by integrating external knowledge retrieval, thereby improving the quality and accuracy of generated content.