AI UAT & Conversational AI Quality

Overview

Built and ran UAT and quality processes for AI-powered conversational systems — virtual assistants and customer service automation where correctness is harder to pin down than a traditional feature test, because the "right answer" often depends on conversational context rather than a fixed expected output.

My responsibilities

• Owned quality strategy for AI systems where correctness isn't binary — building repeatable evaluation frameworks rather than pass/fail scripts
• Worked directly with product and engineering to define what a correct response actually means for a given conversation, not just a single message
• Ran structured UAT passes ahead of releases, then re-tested after model or prompt changes to catch regressions in tone or accuracy
• Prioritized and tracked defects by real user impact, distinguishing genuine failures from acceptable conversational variation
• Reported quality findings back to product and engineering in terms they could act on, not just a raw defect count

What I built & managed

• Designed test scenarios and acceptance criteria specific to conversational AI, not adapted from traditional QA checklists
• Evaluated response quality and edge-case handling across realistic conversation flows
• Managed defect tracking and reporting for issues that only surface in multi-turn conversations
• Assessed user experience quality alongside functional correctness

Technology & tools