<p>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. </p><p>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. </p><p><br></p><p>Listen to our podcast on these platforms: </p><p>• <a href="https://youtube.com/@WisecubeAI/podcasts" target="_blank" rel="ugc noopener noreferrer">YouTube</a>: https://youtube.com/@WisecubeAI/podcasts </p><p>• <a href="https://podcasts.apple.com/us/podcast/gradient-descent-podcast-about-ai-and-data/id1801323847" target="_blank" rel="ugc noopener noreferrer">Apple Podcasts</a>: https://apple.co/4kPMxZf </p><p>• <a href="https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55" target="_blank" rel="ugc noopener noreferrer">Spotify</a>: https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55 </p><p>• <a href="https://music.amazon.com/podcasts/79f6ed45-ef49-4919-bebc-e746e0afe94c/gradient-descent---podcast-about-ai-and-data" target="_blank" rel="ugc noopener noreferrer">Amazon Music</a>: https://bit.ly/4izpdO2 </p><p><br></p><p>Mentioned Materials: </p><p>• <a href="https://youtube.com/@WisecubeAI/podcasts" target="_blank" rel="ugc noopener noreferrer">DSPy Paper</a> - https://arxiv.org/abs/2310.03714 </p><p>• <a href="https://dspy.ai/ " target="_blank" rel="ugc noopener noreferrer">DSPy official site</a> - https://dspy.ai/ </p><p>• <a href="https://github.com/stanfordnlp/dspy" target="_blank" rel="ugc noopener noreferrer">DSPy GitHub</a> - https://github.com/stanfordnlp/dspy </p><p>• <a href="https://www.twosigma.com/articles/a-guide-to-large-language-model-abstractions/" target="_blank" rel="ugc noopener noreferrer">LLM abstractions guide</a> - https://www.twosigma.com/articles/a-guide-to-large-language-model-abstractions/ </p><p><br></p><p>Our solutions: </p><p>- https://askpythia.ai/ - <a href="https://askpythia.ai/ " target="_blank" rel="ugc noopener noreferrer">LLM Hallucination Detection Tool</a> </p><p>- https://www.wisecube.ai - <a href="https://www.wisecube.ai" target="_blank" rel="ugc noopener noreferrer">Wisecube AI</a> platform for large-scale biomedical knowledge analysis </p><p><br></p><p>Follow us: </p><p>- <a href="https://askpythia.ai/" target="_blank" rel="ugc noopener noreferrer">Pythia Website</a>: https://askpythia.ai/ </p><p>- <a href="https://www.wisecube.ai" target="_blank" rel="ugc noopener noreferrer">Wisecube Website</a>: https://www.wisecube.ai </p><p>- <a href="https://www.linkedin.com/company/wisecube/" target="_blank" rel="ugc noopener noreferrer">LinkedIn</a>: https://www.linkedin.com/company/wisecube/ </p><p>- <a href="https://www.facebook.com/wisecubeai" target="_blank" rel="ugc noopener noreferrer">Facebook</a>: https://www.facebook.com/wisecubeai </p><p>- <a href="https://x.com/wisecubeai" target="_blank" rel="ugc noopener noreferrer">Twitter</a>: https://x.com/wisecubeai </p><p>- <a href="https://www.reddit.com/r/pythia/" target="_blank" rel="ugc noopener noreferrer">Reddit</a>: https://www.reddit.com/r/pythia/ </p><p>- <a href="https://github.com/wisecubeai" target="_blank" rel="ugc noopener noreferrer">GitHub</a>: https://github.com/wisecubeai </p><p><br></p><p>#AI #PromptEngineering #DSPy #MachineLearning #LLM #ArtificialIntelligence #AIdevelopment</p>