Gradient Descent - Podcast about AI and Data

The Future of Prompt Engineering: Prompts to Programs


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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


Follow us:

- Pythia Website: https://askpythia.ai/

- Wisecube Website: https://www.wisecube.ai

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- Twitter: https://x.com/wisecubeai

- Reddit: https://www.reddit.com/r/pythia/

- GitHub: https://github.com/wisecubeai


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

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Gradient Descent - Podcast about AI and DataBy Wisecube AI