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Designing Reliable AI Systems with DSPy (w/ Omar Khattab)
In this episode of Neural Search Talks, we're chatting with Omar Khattab, the author behind popular IR & LLM frameworks like ColBERT and DSPy. Omar describes the current state of using AI models in production systems, highlighting how thinking at the right level of abstraction with the right tools for optimization can deliver reliable solutions that extract the most out of the current generation of models. He also lays out his vision for a future of Artificial Programmable Intelligence (API), rather than jumping on the hype of Artificial General Intelligence (AGI), where the goal would be to build systems that effectively integrate AI, with self-improving mechanisms that allow the developers to focus on the design and the problem, rather than the optimization of the lower-level hyperparameters.
Timestamps:
0:00 Introduction with Omar Khattab
1:14 How to reliably integrate LLMs in production-grade software
12:19 DSPy's philosophy differences from agentic approaches
14:55 Omar's background in IR that helped him pivot to DSPy
25:47 The strengths of DSPy's optimization framework
39:22 How DSPy has reimagined modularity in AI systems
45:45 The future of using AI models for self-improvement
49:41 How open-sourcing a project like DSPy influences its development
52:32 Omar's vision for the future of AI and his research agenda
59:12 Outro
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By Zeta AlphaIn this episode of Neural Search Talks, we're chatting with Omar Khattab, the author behind popular IR & LLM frameworks like ColBERT and DSPy. Omar describes the current state of using AI models in production systems, highlighting how thinking at the right level of abstraction with the right tools for optimization can deliver reliable solutions that extract the most out of the current generation of models. He also lays out his vision for a future of Artificial Programmable Intelligence (API), rather than jumping on the hype of Artificial General Intelligence (AGI), where the goal would be to build systems that effectively integrate AI, with self-improving mechanisms that allow the developers to focus on the design and the problem, rather than the optimization of the lower-level hyperparameters.
Timestamps:
0:00 Introduction with Omar Khattab
1:14 How to reliably integrate LLMs in production-grade software
12:19 DSPy's philosophy differences from agentic approaches
14:55 Omar's background in IR that helped him pivot to DSPy
25:47 The strengths of DSPy's optimization framework
39:22 How DSPy has reimagined modularity in AI systems
45:45 The future of using AI models for self-improvement
49:41 How open-sourcing a project like DSPy influences its development
52:32 Omar's vision for the future of AI and his research agenda
59:12 Outro