Introduction to DSPy Framework

Hand-crafted prompts are brittle. A single word change can shift model behavior dramatically, and what works for GPT-4 often breaks on Claude or Gemini.

Prompt engineers spend hours tweaking wording, only to find the changes don't generalize across datasets or model versions. This fragility is the core problem DSPy solves.

DSPy (Declarative Self-improving Python) is a framework from Stanford that treats prompting as a compilation problem rather than a craft problem.

Instead of writing: "Think step by step and answer the following question...", you declare what you want (a signature) and let DSPy figure out how to prompt the model to achieve it.