DSPy: How to Program LLMs Properly
Автор: Yuva D
Загружено: 2026-01-10
Просмотров: 55
DSPy is changing how we build LLM applications.
Instead of writing fragile prompts, DSPy lets you program large language models using Python — with structured inputs, predictable outputs, and automatic prompt optimization.
In this video, we take a deep, beginner-friendly dive into DSPy, covering:
Why prompt engineering breaks at scale
What DSPy actually does under the hood
Signatures and structured outputs
Modules like Predict, Chain-of-Thought, and ReAct
Tool usage with PythonInterpreter and retrieval
Advanced modules like BestOfN and MultiChainComparison
How DSPy optimizers improve prompts using labeled data
This is not a surface-level tutorial.
We focus on mental models, internals, and real reasoning patterns, so you understand why DSPy works — not just how to use it.
If you’re building serious LLM systems, agents, or RAG pipelines, this video will give you a solid foundation.
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