eval-suite-design

Designing an AI evaluation suite involves identifying failure categories from error analysis, defining type-specific evals like LLM-as-judge, and outlining a three-layer architecture to monitor product quality. This process produces a complete, runnable eval suite for reliable AI features.

The LLM-as-judge approach for AI quality assurance uses subjective LLM judgments alongside deterministic code-based checks to evaluate failure modes. It provides tailored prompts to assess complex AI outputs, ensuring robust product quality evaluation where simple pass/fail checks are insufficient.

Structuring pre-commit, pre-deploy, and production monitoring for AI evals requires a three-layer architecture that runs deterministic and subjective checks at each development stage. This operationalized run plan tracks quality improvements and catches failures before they reach production.

You can use code-based checks and human evaluation together in an AI eval suite by defining type-specific evals for each failure category. Combining deterministic code checks with subjective human judgments and LLM-as-judge prompts ensures comprehensive failure analysis and product reliability.

You need a structured evaluation suite for your AI product feature when teams struggle to reliably identify failure modes, delaying iteration and risking quality. Implementing targeted eval designs and a three-layer run plan operationalizes measurement to track improvements over time.