An evaluation & regression suite for LLM features

Challenge

LLM features are deceptively fragile. A prompt tweak, a model upgrade, or a logic change can quietly degrade quality — and without measurement, no one notices until a customer does. Teams shipping AI need a way to know whether a change made things better or worse, before it ships.

Approach

We designed an evaluation and regression framework that benchmarks agent and model outputs against gold-standard datasets, scores them automatically, and tracks how quality moves across every prompt, model, and logic change. It runs in the delivery loop, so regressions surface as part of normal development rather than in production.

System design

• Gold-standard evaluation datasets per feature and task
• Automated scoring against expected outputs and quality criteria
• Regression tracking across prompt / model / logic changes
• “Red flag” detection for unsafe or incorrect outputs, with analytics

What we delivered

• A reusable evaluation harness adopted across multiple LLM features
• Regression reports that compare candidate changes against a baseline
• Analytics that make quality trends legible over time
• A safety net that lets teams ship changes with confidence

Why it mattered

This is the discipline that separates AI that improves from AI that silently rots. By making quality measurable and regressions visible, the suite keeps LLM features stable as they evolve — and gives teams the confidence to move quickly without breaking what works.