Creating Synthetic Evaluation Benchmarks with LLM-as-a-Judge

How to generate diverse adversarial test cases and establish automated G-Eval evaluation metrics for enterprise apps.

Prerequisites

Python
Familiarity with precision and recall metrics

Step 1: Step 1: Persona-Driven Scenario Generation

Generate hundreds of realistic user personas with distinct emotional states, technical proficiencies, and adversarial edge cases.

Step 2: Step 2: Calibrating the LLM Judge Metric

Align the judge model by feeding 50 human-annotated examples with detailed rubrics before running automated regression tests.