RAG vs Fine Tuning: Key Differences Explained | RAG vs Fine-Tuning For LLMs | #Shorts | #Simplilearn
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LLM Foundations90%
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Explains the key differences between RAG and fine-tuning for LLMs
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This video on RAG vs Fine-Tuning: Key Differences Explained by Simplilearn will help you understand two of the most important techniques used to enhance large language models (LLMs). The tutorial begins by explaining Retrieval-Augmented Generation (RAG), where external knowledge sources are connected to an AI model to provide up-to-date and context-specific information without retraining the model. You will also learn about fine-tuning, a process that retrains a pre-trained model on specialized datasets to improve performance for specific tasks or domains. The video compares RAG and fine-tuning across factors such as cost, implementation complexity, scalability, maintenance, accuracy, and use cases. You will understand when to use RAG, when fine-tuning is more appropriate, and how organizations combine both approaches to build powerful AI applications. By the end of this tutorial, you will clearly understand the differences between RAG and fine-tuning and how they are applied in modern LLM-powered systems.
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