Prompt optimization is the systematic process of improving prompts to get better, more consistent, or more cost-effective outputs from language models. It involves A/B testing different phrasings, measuring output quality against benchmarks, and iterating. Automated prompt optimization tools can search through prompt variations using algorithms. For production systems, optimized prompts reduce hallucinations, lower token costs, and improve task accuracy. It is a continuous practice, not a one-time setup.