Prompt optimization frameworks treat prompts as artifacts that can be improved through automated search and evaluation.
Examples
- DSPy: declarative modules + optimizers that tune instructions and demonstrations.
- OPRO: use an LLM as an optimizer by iterating on prompt candidates with scores.
Why It Matters
Manual prompt tweaks do not scale. For agents, you want evaluation-driven optimization tied to metrics like success rate, cost, and latency.