Writing good prompts is often an iterative process. You start with a rough prompt, test it on different inputs, notice where it fails, refine the instructions, and repeat. This manual loop works for small experiments, but it becomes slow and difficult when you are building production systems that depend on LLMs.

This is where prompt optimization comes in.

Prompt optimization treats prompts as tunable components of a system rather than fixed strings. Instead of relying only on human intuition, you evaluate prompts against datasets, measure performance, and systematically improve them. The goal is to find prompt structures that consistently produce high-quality results across many inputs.

One of the most interesting tools in this space is DSPy.

In this chapter, we will explore how prompt optimization works and how frameworks like DSPy help automate this process.