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Deep Dive

DSPy introduces signatures (to abstract prompts), modules (to abstract prompting techniques), and optimizers that can tune the prompts (or weights) of modules.

📄️ DSPy Assertions

Language models (LMs) have transformed how we interact with machine learning, offering vast capabilities in natural language understanding and generation. However, ensuring these models adhere to domain-specific constraints remains a challenge. Despite the growth of techniques like fine-tuning or “prompt engineering”, these approaches are extremely tedious and rely on heavy, manual hand-waving to guide the LMs in adhering to specific constraints. Even DSPy's modularity of programming prompting pipelines lacks mechanisms to effectively and automatically enforce these constraints.