dspy

Hand-tuned prompts break on the next model upgrade and fail to compose across pipeline stages. Frameworks like DSPy compile prompts against a metric instead. The signal is treating prompting as an optimized program. Here is the answer.

Letting a model write your prompts works, right up until the optimizer memorizes your eval set and hands you an unreadable prompt that scores better and generalizes worse. The bounds are the whole answer.