In the context of AI development, setting up automated evaluations is crucial for efficiently assessing the impact of modifications to large language model (LLM) applications, as demonstrated using Langfuse. This guide emphasizes the importance of distinguishing between prompt-related errors and model limitations, advocating for automated evaluators to address the latter. It provides a framework for creating scalable evaluators, such as the LLM-as-a-Judge, which can be configured in the Langfuse UI or through custom code. The process involves drafting precise prompts, validating evaluators against human judgment using metrics like True Positive Rate (TPR) and True Negative Rate (TNR), and integrating these evaluations into a CI/CD pipeline. By consistently scoring application performance and monitoring failure modes, developers can iterate faster while maintaining high quality, ultimately improving the application’s reliability and effectiveness.