How to evaluate large language models using Prompt Engineering | Testing and Improving with PyTorch

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In this second video of the Prompt Engineering course, we'll dive into evaluating and testing prompt engineering models. You'll learn different metrics for evaluating prompt engineering models, techniques for debugging and improving them, and ways to test them on different datasets. We'll also provide practical exercises that will help you evaluate and test prompt engineering models, giving you hands-on experience using different metrics to evaluate your models, debugging and improving them, and testing them on different datasets.

🚀 Learnings:

🤖 Different metrics for evaluating prompt engineering models, including perplexity, accuracy, and human evaluation.
🤖 Techniques for debugging and improving prompt engineering models by analyzing generated responses and identifying common errors or patterns.
🤖 The importance of testing prompt engineering models on different datasets to evaluate their ability to generalize to new and unseen data.
🤖 Tools and techniques, such as visualization tools and cross-validation, that can be used for evaluating and testing prompt engineering models.
🤖 Ongoing evaluation and testing of prompt engineering models to ensure continued performance.

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