How to Set Up AI Evaluation for LLM Apps

AI evaluation for LLM apps is essential in determining the readiness of an application for deployment, focusing on repeatable tests, clear scoring, versioned results, and production feedback integration. The setup process involves defining specific and testable application behaviors, creating a small but realistic evaluation dataset that includes both happy-path and edge-case scenarios, and separating app prompts from reference answers to ensure unbiased evaluation. Clear, consistent scoring criteria are essential, and multiple evaluation methods, including deterministic checks, reference-based comparison, and LLM grading, should be employed. A baseline should be established before any changes to prompts or models, with cost and latency also considered as crucial factors. Versioning of prompts, models, datasets, and configurations is necessary for reproducibility, and evaluation processes should align with development and release workflows to ensure continuous improvement. Production traces are vital for refining datasets, and the evaluation should grow alongside development, integrating real-world failures into future test cases. PromptLayer offers a platform to manage these processes, enabling AI teams to develop a reliable evaluation workflow.

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