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Fine-Tuning with OpenAI in a Real World App with Mark Shenouda

In this training, Mark Shenouda explores the OpenAI’s fine tuning feature. We’ll learn how to do implement fine tuning in a Next.js application. Fine-tuning involves providing specific examples and adjusting the model's behavior to improve its understanding and responses. By exposing the model to a wide range of data, it learns to generate more accurate and contextually appropriate answers. This process enables AI to grasp the intricacies of various tasks.

This training illustrates the clear distinction between fine-tuning and RAG. RAG relies on retrieving pre-existing information, while fine-tuning takes a step further by training the model on specific data. This approach is highly effective for tasks requiring a deep understanding of particular domains or datasets. By fine-tuning AI models, developers can create products that not only understand their domain's nuances but also provide relevant and helpful responses, saving valuable time and resources.

Despite its advantages, fine-tuning comes with challenges, notably the cost implications due to the extensive training data required. Fine-tuning demands a substantial amount of data to ensure the model's accuracy and effectiveness. Additionally, data preparation is crucial, involving organizing the data in a structured format, to facilitate seamless integration with the AI model. While fine-tuning enhances AI performance, RAG is also effective in certain scenarios. Evaluating the specific requirements of each task and determining the most suitable approach allows developers to optimize AI performance and achieve the desired outcomes.

Watch this episode of YouTube.

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