Selected Systems
Conversational AI Quality & Evaluation
Systematic quality measurement for large-scale conversational AI
- Problem
- Conversational systems built on large language models can degrade in ways that are invisible to standard uptime and latency monitoring — a model can respond instantly and still be wrong, off-tone, or ungrounded.
- Approach
- Designed an evaluation layer that scores conversations against defined quality dimensions — correctness, groundedness, tone, and task completion — combining automated LLM-based judges with structured human review, so regressions are caught before they reach production traffic.
- Outcome
- A repeatable evaluation pipeline that turns "does the model seem fine" into a measurable, versioned signal the team can track release over release.