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Prompt Like a Data Scientist: Auto Prompt Optimization and Testing with . . . To address these issues, Stanford NLP has published a paper introducing a new approach with prompt writing: instead of manipulating free-form strings, we generate prompts via modularized programming The associated library, called DSPy, can be found here
DSPy: Machine Learning Attitude Towards LLM Prompting DSPy emphasises programming over prompting It unifies techniques for prompting and fine-tuning LMs as well as improving them with reasoning and tool retrieval augmentation, all expressed through a minimalistic set of Pythonic operations that compose and learn
GitHub - stanfordnlp dspy: DSPy: The framework for programming—not . . . DSPy stands for Declarative Self-improving Python Instead of brittle prompts, you write compositional Python code and use DSPy to teach your LM to deliver high-quality outputs Learn more via our official documentation site or meet the community, seek help, or start contributing via this GitHub repo and our Discord server
How to 10x Your LLM Prompting With DSPy | Lusera Tech Enter DSPy, a groundbreaking framework that’s set to revolutionize how we work with LLMs We’ll explore how DSPy can help you dramatically enhance your LLM prompting efficiency and effectiveness DSPy isn’t just another tool in the AI developer’s toolkit—it’s a paradigm shift
Guide on Prompting with DSPy - Analytics Vidhya Learn how DSPy automates prompt engineering and optimizes performance for complex tasks Explore practical examples of DSPy in action, such as math problem-solving and sentiment analysis Discover the advantages of DSPy, including modularity, scalability, and continuous self-improvement
DSPy Tutorial - IBM DSPy is an open source Python framework for building large language model (LLM) applications and fine-tuning their performance through code rather than one-off techniques for prompt optimization A DSPy program provides a modular way to configure and fine tune LLM applications by optimizing prompts to get accurate outputs