Prompt Engineering Vs RAG Vs Finetuning Explained Easily

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Prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning are three distinct techniques used to enhance the performance of large language models. Prompt engineering involves designing effective input prompts to guide the model’s responses without altering its internal parameters, making it a fast and lightweight approach. RAG, on the other hand, supplements the model’s capabilities by retrieving relevant external documents at runtime, allowing the model to generate informed answers even about topics not seen during training. Fine-tuning involves training the model further on domain-specific data, modifying its weights to specialize it for particular tasks or industries. While prompt engineering is quick and easy, RAG offers dynamic access to up-to-date knowledge, and fine-tuning provides the highest level of customization but requires significant resources.
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