AI quality assurance · Content evaluation · Digital product QA
Lisa Marie Kocian
AI Quality Assurance & Content Evaluation Specialist
Evaluating AI output. Improving content quality. Building better digital experiences.
I work at the intersection of AI quality and content evaluation — combining AI output evaluation, prompt iteration, content review, and human judgment to identify problems, improve clarity, and build more reliable digital experiences.
Better AI products for real people.
Selected work
A selection of projects where I have contributed to AI quality, content evaluation, and digital product QA, from e-commerce experiences to mission-driven resources.
Commerce Application & Workflow QA
AI-assisted commerce development with Shopify integration and end-to-end review of the customer journey, from product discovery through checkout, with documented defects and quality improvements.
Content Quality & Youth Resource Usability
An educational support resource focused on clear guidance, accessible navigation, and careful review of AI-assisted content.
Skills
The same iterative evaluation loop applied to both AI-output review and digital-commerce testing.
Output evaluation, accuracy review, consistency, guideline adherence, error identification, hallucination awareness, edge-case thinking, human review, user-impact evaluation.
Prompt refinement, comparison testing, instruction clarity, output evaluation, failure-pattern identification, systematic iteration, feedback loops.
Product discovery, customer journey, navigation, mobile usability, product presentation, cart behavior, checkout friction, conversion thinking, visual QA, information architecture.
Identifying repetitive work, AI-assisted workflows, automation opportunities, content/research workflows, operational efficiency, human-in-the-loop review, implementation testing.
Storefront systems, customer data, analytics, AI tools, marketing workflows, product information, user experience, third-party platforms, interconnected processes.
Defect identification, reproducible observations, QA documentation, testing notes, before/after comparison, prioritization, troubleshooting, iteration, actionable recommended fixes.
Professional process
The same iterative loop applied to both AI-output evaluation and digital-commerce testing.
Experience
Tools
Technologies listed reflect practical working knowledge gained through applied projects, not expert engineering experience in every tool.
Methods
Credentials
Programs shown with a year are completed; the rest are in progress or in preparation.
For opportunities in AI evaluation, content review, and digital product QA, contact me by email.
