Prompt example extracted from DSPy Examples.
# Program, don’t prompt DSPy is a declarative way to build with LLMs. We describe our tasks as structured inputs and outputs. Often, we compose multiple tasks into multi-step programs and agents where each piece stays independently inspectable, swappable, and tunable. DSPy handles prompt construction, context management, and optimization that tunes each step to improve the whole program. When a step needs to act on the world (to search, fetch, or compute), we give it tools as plain Python functions. Functions also serve as metrics: you define one to grade the output, and DSPy generates a better program accordingly. The optimizer tunes the prompt for any model you pick, so a small, cheap model can often match or beat a hand-prompted frontier one.. DSPy helps us program LLMs, rather than prompting them, creating modular, maintainable and optimizable AI software.
