Fine-Tuning vs Retrieval Augmented Generation

Large Language Models (LLMs) are increasingly used for question-answering with custom data, utilizing techniques like fine-tuning and Retrieval Augmented Generation (RAG) to enhance their capabilities. Fine-tuning involves adjusting a pre-trained model to new data, allowing the model to learn additional knowledge but at the risk of "catastrophic forgetting" and higher costs. In contrast, Vectara's Grounded Generation, a form of RAG, uses semantic retrieval to provide context without altering the LLM, offering advantages such as easy updates, cost-effectiveness, and data privacy. While fine-tuning is suitable for stable datasets, RAG is more adaptable for dynamic data, providing real-time updates and maintaining data control. Additionally, RAG can cite sources and offer granular access controls, making it a versatile choice for building specialized LLM applications.

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