The growing capabilities of generative AI have created a "confidence gap" where companies are hesitant to trust it with critical tasks due to concerns about reliability, despite its potential for competitive advantage. Traditional evaluation metrics, such as BLEU and ROUGE, are insufficient for modern large language models as they focus on surface-level similarity rather than actual meaning, leading to inaccurate assessments of AI performance. To address this issue, custom metrics tailored to specific business goals and workflows can be developed, leveraging large language models as judges to evaluate complex criteria like empathy, compliance, and tone. Continuous Learning from Human Feedback (CLHF) is also essential, allowing domain experts to provide targeted feedback that improves the evaluation system over time, enabling organizations to define and measure quality in a way that aligns with their unique needs and values. By adopting custom evaluators and CLHF, companies can build trust in their AI systems and move from tentative experiments to confident deployments, ultimately bridging the confidence gap and unlocking the full potential of generative AI.