Learn DSPy, a Pythonic framework for building robust AI systems without brittle prompts. Define tasks with signatures, compose modules, evaluate with metrics, and optimize prompts or models—all in an iterative, modular workflow.
The course introduces DSPy—its installation, programming, evaluation, and optimization for building AI systems. It covers using LMs, designing signatures, and composing modules for advanced tasks.
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11 practices
You will learn the evaluation steps — collecting development data, defining DSPy metrics, and running evaluations — while become proficient in data handling with Example objects and creating metrics to assess output quality.
Learn DSPy optimization tuning prompts and LM weights via few-shot learning, instruction optimization, and finetuning. You will cover data splits, various optimizers, and saving/loading optimized programs for iterative improvement.
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