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DSPy - ideas to improve RAG
What makes DSPy unique? How is DSPy different from LangChain or LlamaIndex? 1
- DSPy is a framework developed by Stanford University that can automatically
optimize LLM prompts and weights.
- DSPy is conceptually similar to PyTorch. You define modules in your program, treat the prompts you use as weights for the model, and train the optimal prompts on the training data. In DSPy, this training step is called “Compile”.
- LangChain, LlamaIndex, and DSPy are frameworks that help developers easily
build applications based on LMs (Language Models)
- The typical LangChain and LlamaIndex pipeline usually involves prompt templates, making the entire pipeline sensitive to component changes.
In contrast, DSPy shifts the construction of LM-based pipelines from operating prompts to being closer to programming.
In short, the advantage of using DSPy is that you don’t have to write the prompts yourself, it will automatically adjust the prompts behind the scenes if the data is available