fine tuning llms vs rag important usecases

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Fine tuning LLMs vs RAG for important use cases

LLMs (Large Language Models) are pre-trained models that can be fine-tuned for specific tasks, offering high performance on a wide range of NLP tasks.
RAG (Retrieval-Augmented Generation) models combine retrieval and generation capabilities, allowing for more context-aware responses.
LLMs are suitable for tasks requiring general language understanding, while RAG models excel in tasks where context and external knowledge are crucial.
LLMs are commonly used for text generation, sentiment analysis, and language translation, while RAG models are effective in question answering, summarization, and dialogue systems.
Fine-tuning LLMs can be computationally expensive but yields high performance, while RAG models require efficient retrieval mechanisms for optimal performance.

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