Building Smarter LLM Agents with DSPy: Optimized Prompting & Automation

About the Event

Discover how to build self-improving LLM agents with DSPy, a powerful framework for structuring, optimizing, and refining prompts. This session will introduce the DSPy programming model, helping you go beyond traditional prompting by optimizing module interactions and enhancing prompt efficiency through programmatic control. Learn how DSPy’s compiler and optimizers enable seamless adaptation across different models while automating refinements. We'll also explore real-world challenges and how Pypeai.com leverages DSPy to help builders experiment, migrate to cost-effective models, and continuously improve agent performance. Don't miss this hands-on deep dive into smarter AI workflows!

Key Takeaways:

A solid understanding of DSPy’s capabilities and workflow
Practical techniques to enhance prompt quality and efficiency
Insights into automated prompt optimization strategies
Best practices for integrating DSPy into real-world AI systems

About the Speaker

Ashish Tripathy

CTO at Pype

Ashish is currently developing pypeai.com, which helps developers to migrate their AI agents to private and cheaper models without compromising on accuracy. With over 12 years of experience in Data/ML/AI, his work at companies like LinkedIn and SAP includes machine learning solutions for online fraud, content abuse, multi-agent frameworks for workflow automations. He is an advocate for implementing engineering practices in prompt engineering. Additionally, Ashish holds patents in user behaviour profiling and large-scale duplicate content detection in social media.

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Building Smarter LLM Agents with DSPy: Optimized Prompting & Automation