In 2025, the reliability of Large Language Models (LLMs) crucially depends on the evaluation and optimization of prompts, making robust prompt evaluation frameworks essential for maintaining production reliability. This guide outlines the top frameworks available, including Helicone, OpenAI Eval, Promptfoo, Comet Opik, PromptLayer, Traceloop, and Braintrust, each offering unique features like open-source options, production monitoring, and custom evaluations. Developers face challenges like ineffective prompt engineering, unpredictable outputs, and the need for specialized tools to refine prompts systematically. Key metrics for assessing prompts include output accuracy, relevance, coherence, format adherence, latency, and cost efficiency. Each framework has its differentiators, such as Helicone's real-time insights and ease of integration, OpenAI Eval's rigorous benchmarking, and Promptfoo's test-driven development approach. While choosing a framework, considerations should include core features, integration compatibility, scalability, team size, metrics support, and usability. These frameworks have evolved from basic testing tools into comprehensive platforms that help manage, monitor, and optimize AI interactions, ensuring reliable and valuable production-grade LLM applications.