🌟 Intro to DSPy (10 mins)
- 🚀 Learn about the motivation behind DSPy, a new programming model for optimizing language model (LM) pipelines.
- 🔧 Understand how DSPy abstracts away complex prompt engineering using modular, declarative operators.
- 🤖 Discover how the DSPy compiler can automatically optimize multi-stage LM systems.
🎯 Use Cases (10 mins)
- 🕵️♂️ Explore the types of NLP tasks that DSPy is particularly well-suited for.
- ⚖️ Gain insights into when DSPy might be less applicable, such as for simpler tasks that only require a single LM call.
- 🎓 Understand the target audience for DSPy, primarily researchers and practitioners building state-of-the-art LM pipelines.
🛠️ Modular Approach (10 mins)
- 📦 Dive into the three key abstractions introduced by DSPy: signatures, modules, and teleprompters.
- 🧩 Learn how these abstractions enable the composition of arbitrary directed acyclic graphs of LM operators.
- 🌐 Understand how DSPy draws inspiration from popular deep learning frameworks like PyTorch.
- 🚀 Discover how DSPy’s modular approach facilitates systematic exploration of complex LM pipeline design spaces.
🚧 Limitations (5 mins)
- 💡 Gain awareness of DSPy’s current limitations, such as its reliance on an underlying LM and its focus on text-only pipelines.
- 🤔 Understand the computational challenges associated with rejection sampling-based teleprompters.
- 🌱 Learn about areas for future improvement, such as explicit support for controlling hallucination and enhancing truthfulness.
👥 Agents & DSPy (5 mins)
- 🎭 Learn how DSPy programs can express agent-like behavior through ReAct-style modules.
- 🔍 Understand the parallels between signature graphs and the action spaces of goal-driven agents.
- 🚀 Explore the potential for DSPy to support more dynamic reasoning through test-time bootstrapping and backtracking.
- 🌟 Discover how DSPy may help scale up agent capabilities by composing specialized skill modules.
🎉 Demo time (10 mins)
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❓ Q&A (10 mins)
