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Exploring methods to embed knowledge into AI models is essential for creating intelligent and reliable systems. This can be achieved through fine-tuning models and using prompt-based knowledge retrieval via Retrieval-Augmented Generation (RAG). Let's dive into these methods in more detail. Fine-Tuning Models Fine-tuning involves embedding specific knowledge directly into the model’s weights. This approach allows the AI to retrieve precise information, making it ideal for applications requiring high accuracy. However, it’s a complex process that demands meticulous preparation of training data. For example, fine-tuning an AI model to assist doctors in diagnosing diseases requires a large dataset of annotated medical records. This ensures the model accurately understands and predicts health conditions. By embedding vast amounts of medical literature and patient data, the AI can provide highly accurate diagnostic suggestions. However, preparing this data is labor-intensive and requires...