Case StudyAI quality assurance

Internal AI chatbot QA and retrieval validation

Testing-led quality improvements for internal IT and HR assistant workflows.

Repeatable regression posture for support assistants

Role

AI quality assurance lead

Team context

Internal assistant quality program spanning IT and HR support use cases, coordinated with platform owners and knowledge maintainers.

Responsibility scope

Designed regression loops for retrieval quality, edge-case handling, and safe-response behavior.
Translated observed assistant failures into retrieval tuning and knowledge updates.
Defined rollout-readiness checks for practical IT and HR support scenarios.

Stakeholders

Decision points

Test retrieval and safe-response behavior before rollout rather than after trust was lost.
Tie failed responses back to knowledge updates instead of ad hoc prompt tweaks.
Create a repeatable QA posture that future assistant releases could inherit.

Problem and constraints

Improve retrieval quality and rollout readiness for an internal AI assistant supporting employee IT and HR requests.

Architecture approach

Lead QA and test loops focused on retrieval quality, edge-case behavior, and safe responses.
Iteration process connecting observed failures to retrieval tuning and knowledge updates.
Rollout-readiness checks for practical support scenarios across internal operations.

Outcomes

Next iteration

Introduce a formal regression scorecard to track retrieval and safety performance across releases.