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Learn to leverage DSPy for automated prompt engineering to transform mediocre baselines into high-performing machine learning pipelines through this 18-minute tutorial. Discover how to use a powerful "prompt model" to teach smaller, faster "task models" to excel at financial sentiment analysis by automatically optimizing prompts rather than manually crafting them. Explore the fundamentals of DSPy and its approach to prompt optimization, then dive into hands-on implementation starting with notebook setup and baseline evaluation of a financial news semantic analysis system. Progress through the complete workflow of prompt optimization techniques that can significantly improve model performance without requiring extensive manual prompt engineering. Master the practical application of DSPy's automated optimization capabilities to enhance your natural language processing pipelines for financial data analysis and sentiment classification tasks.
