Explore the evolution of prompt engineering in this episode of Gradient Descent. Manual prompt tuning — slow, brittle, and hard to scale — is giving way to DSPy, a framework that turns LLM prompting into a structured, programmable, and optimizable process.

Learn how DSPy’s modular approach — with Signatures, Modules, and Optimizers — enables LLMs to tackle complex tasks like multi-hop reasoning and math problem solving, achieving accuracy comparable to much larger models. We also dive into real-world examples, optimization strategies, and why the future of prompting looks a lot more like programming.

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Mentioned Materials:

• DSPy Paper - https://arxiv.org/abs/2310.03714

• DSPy official site - https://dspy.ai/

• DSPy GitHub - https://github.com/stanfordnlp/dspy

• LLM abstractions guide - https://www.twosigma.com/articles/a-guide-to-large-language-model-abstractions/

Our solutions:

- https://askpythia.ai/ - LLM Hallucination Detection Tool

- https://www.wisecube.ai - Wisecube AI platform for large-scale biomedical knowledge analysis

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#AI #PromptEngineering #DSPy #MachineLearning #LLM #ArtificialIntelligence #AIdevelopment