Microsoft & LinkedInIn progress
Career Essentials in Generative AI
Generative AI foundations, responsible use, and practical workplace applications
AI Quality Assurance · AI Commerce · Content Evaluation
AI Quality Assurance + AI Commerce
I test AI output, validate content quality, iterate on prompts, and QA e-commerce applications — catching accuracy issues, inconsistencies, and broken workflows before they reach users. My work combines AI output evaluation, document quality review, and hands-on digital product implementation, including a Shopify-integrated commerce application built with Base44 and React. I am currently part of the Alignerr network by Labelbox, a global community of experts who actively research and shape the future of AI.
Alignerr Network·LabelboxQA Inspection
LiveOUTPUT VALIDATION
Product naming consistent
METADATA QA
Legacy terminology detected
CONTENT QA
Instructions followed
CHECKOUT FLOW
Route verified
PROMPT TEST
Revised instruction
- AI Quality & LLM Evaluation
- Prompt & Instruction Testing
- E-Commerce QA
- Defect Documentation
- Responsive Web Testing
- Source-to-Output Validation
QA Methodology
How I evaluate quality
01
Accuracy
Does it actually work and contain correct information?
Whether reviewing AI-generated text, a product page, a metadata tag, or a checkout flow — the first question is always: is this factually correct, functionally working, and free of unsupported claims.
Does it hold up across every surface and state?
Terminology, pricing, product names, metadata, visual layout, instruction adherence — inconsistency across pages, devices, or AI outputs is a quality defect even when no single instance is individually wrong.
Has the behavior been verified, not assumed?
Testing means actually performing the action: clicking the button, reading the output, checking the route, running the query. Assumptions about how something works are the most common source of shipping defects.
Professional Credential
Shaping the Future of AI
I am part of the Alignerr network by Labelbox — a global community of vetted domain experts who actively research, evaluate, and refine AI model output. Through expert-in-the-loop evaluation, Alignerr members provide the human feedback that directly improves AI accuracy, safety, and alignment. This credential reflects hands-on work evaluating AI-generated content, identifying defects, and supplying structured corrective feedback that shapes how models learn.
Human-in-the-Loop Evaluation
Evaluating, rating, and refining AI model outputs to improve accuracy, coherence, and alignment with human intent.
Quality & Safety
Identifying hallucinations, unsupported claims, and inconsistencies before AI output reaches end users.
Shaping AI Training
Providing expert feedback that directly informs model training, fine-tuning, and reinforcement learning.
Global Expert Network
Part of a vetted worldwide community of domain specialists selected to research and shape the future of AI.
Functional E-Commerce Application
Building Torch Republic from brand concept to working commerce application
E-commerce Application · Product Design · QALive Project · Continuous QA and Iteration
I built Torch Republic as a functional direct-to-consumer coffee application — not simply a marketing website. The project combines a custom Base44/React customer experience with Shopify’s commerce infrastructure, supporting live product information, persistent cart behavior, secure checkout, responsive interactions, analytics, and ongoing quality assurance.
- Role
- Founder, Product Builder, E-commerce QA
- Application
- Custom DTC storefront and commerce experience
- Core stack
- Base44, React, Shopify Storefront API, GA4, Google Search Console
- Responsibilities
- Product strategy, UX design, application configuration, API integration, content systems, responsive QA, commerce testing, troubleshooting, and iterative optimization
Product strategyBusiness requirementsE-commerce architecture decisionsAI-assisted developmentPrompt iterationContent architectureCustomer-experience designApplication configurationAPI integrationContent systemsResponsive QACommerce testingTroubleshootingIterative optimization
Skills Demonstrated
AI-assisted developmentQA testingE-commerce architectureReact / application troubleshootingUser journey testingAccessibility reviewSEO QAMetadata QAContent QAAnalytics implementationPrompt iterationConversion analysisDocumentation
Not Just a Website
More than a storefront
Torch Republic was developed as a working commerce application with interconnected customer, product, cart, analytics, and checkout workflows. I was responsible for shaping the brand experience while also validating how the application behaved across devices, sessions, integrations, and real shopping scenarios.
Live Commerce Integration
Connected the custom storefront to Shopify commerce infrastructure so products, pricing, cart actions, and checkout operate as part of a real purchasing flow.
Application State and Cart Behavior
Implemented and tested customer-facing states including add-to-cart feedback, item quantities, cart persistence, removal, accurate cart counts, loading states, and error recovery.
Responsive Product Experience
Designed and validated the application across mobile and desktop, including navigation, product presentation, modal behavior, interactive elements, image handling, and checkout transitions.
Analytics and Discoverability
Configured GA4, Google Search Console, metadata, and SEO foundations to support measurable traffic, search visibility, behavioral analysis, and future optimization.
Application Architecture
How the systems connect
The custom application controls the branded browsing and shopping experience. Shopify provides the underlying commerce services and secure checkout. The integration allows the two systems to operate as one customer journey.
01
Customer InterfaceMobile and desktop browsing experience
02
Base44 / React ApplicationCustom storefront, cart state, content, analytics
03
Shopify Storefront APILive product data, pricing, availability
04
Shopify Cart and Secure CheckoutPayment processing handled by Shopify
The custom application does not collect or store payment-card information. Shopify processes all payments through its secure checkout infrastructure.
Verified Functionality
What the application does
Each item below reflects functionality present in the current codebase. No planned features are presented as completed work.
- Presents live coffee products and accurate pricing
- Provides reusable product and commerce components
- Adds products to a functioning cart
- Updates quantities and removes cart items
- Preserves cart state across relevant navigation or refresh behavior
- Routes customers into Shopify’s secure checkout
- Handles loading, success, empty, and error states
- Supports responsive mobile and desktop experiences
- Integrates customer-facing promotional components
- Tracks meaningful customer interactions through analytics
- Provides optimized product content and search metadata
A weighted promotional prize experience (Spin to Win) with email validation, persistence rules, accessible interaction states, and Shopify-compatible discount handling has been implemented in the codebase. It is pending discount-code configuration before production activation.
AI-Assisted Development Loop
AI-Assisted Development ≠ Unreviewed AI Output
The process depended on continuous human evaluation. Generated outputs were reviewed against business requirements, customer experience, visual consistency and functional behavior before being accepted or revised.
- Business Requirement
- Prompt / Instruction
- AI-Assisted Build
- Manual Review
- Defect Discovery
- Prompt / Implementation Revision
- Retest
- Deploy
Engineering & QA
Building, testing, and improving the system
Building the application required continuous validation beyond visual design. I tested complete customer journeys, investigated inconsistent application states, documented defects, traced integration problems, and translated findings into precise implementation requirements.
Commerce QA
- Product and price validation
- Cart-count accuracy
- Quantity and removal behavior
- Cart persistence
- Checkout handoff
- Shipping-state validation
Interface QA
- Mobile and desktop responsiveness
- Navigation and dropdown behavior
- Modal and popup control
- Image loading
- Layering and overflow
- Focus and accessibility behavior
Integration QA
- Storefront API responses
- Shopify product status
- Product availability
- Error and loading states
- Analytics verification
- Production-versus-preview behavior
Content QA
- Product-description accuracy
- Offer consistency
- Metadata validation
- Encoding and formatting issues
- Removal of outdated product references
- Customer-facing compliance language
I used AI-assisted development as part of the build process, while remaining responsible for product decisions, prompt specifications, workflow design, output validation, defect identification, regression testing, and final quality.
QA Evidence
From Observation to Improvement
Real examples from the project, walked through the way I actually work — observe, hypothesize, change, validate. No invented metrics; outcomes are described qualitatively.
Broken product imagery across the store
- Problem
- Product image components pointed at hardcoded Shopify placeholder URLs that no longer resolved.
- Evidence
- Image onError handlers fired on live pages, leaving broken-image icons where product photos should be.
- Hypothesis
- The hardcoded URLs were stale and never validated against the live image source.
- Change
- Removed the hardcoded URLs and standardized image handling on a verified, reachable fallback plus valid image sources.
- Validation
- Re-checked affected product, collection and gallery views across pages.
- Outcome
- Consistent product imagery sitewide with no broken-image states.
Defect Thinking
How a single issue gets framed
QA is more than “something looks off.” It is defining the issue, the impact, the priority, the action and how the fix is verified.
- issue
- Homepage Add to Cart button failed silently — clicking a product did not add it to the cart, show a confirmation, or update the cart count.
- impact
- Customers could not purchase products from the homepage. The cart count stayed at zero and no error or confirmation appeared, making the button appear non-functional.
- priority
- Critical / Conversion
- rootCause
- The cart confirmation and cart-count update were gated behind a successful Shopify Storefront API response. When the browser-side SDK call to the backend function failed or returned an error, the optimistic cart update was rolled back and no confirmation was shown — the user saw nothing happen.
- action
- Restructured the cart flow to show the confirmation card and update cart state immediately (optimistic), then sync to Shopify in the background. If the sync succeeds, local items are enriched with Shopify-confirmed pricing and line IDs. If it fails, the item stays in the local cart and checkout creates a fresh Shopify cart from local state — no data is lost.
- validation
- Regression tested Add to Cart from homepage, shop page, and product detail page. Verified cart count updates from 0 to 1 on first click, confirmation card appears, cart drawer opens, quantity controls work, item removal works, and checkout handoff creates a correct Shopify cart with matching product, quantity, and price.
- outcome
- Add to Cart now works reliably from every entry point. The user always sees immediate feedback, and the cart remains functional even during transient Shopify API issues.
Case-Study Challenges
Problems I identified and resolved
These are concise examples from the build. Some were implemented with AI-assisted development tools; in every case I was responsible for identifying, testing, documenting, and validating the outcome.
- 01Diagnosed cart-count discrepancies where one customer action could produce an incorrect item total
- 02Corrected inconsistent pricing between product displays and cart states
- 03Investigated product availability and Shopify draft-versus-active status
- 04Improved modal dismissal and prevented promotional overlays from blocking shopping
- 05Found responsive image and logo problems that appeared differently on desktop and mobile
- 06Identified corrupted text characters and separated source-content issues from cached production behavior
- 07Audited exposed utility or administrative routes and documented production-access risks
- 08Simplified the catalog as the product strategy evolved from multiple concepts to two core coffee products
Additional Case Study
AI Output Validation & Content QA
The same methodology — accuracy, consistency, testing — applies whether I am evaluating an AI-generated product page or auditing metadata for an entire site. This example shows how I caught and corrected AI-introduced errors in global SEO identity.
What I Tested
- Factual accuracy of metadata against the current product catalog
- Instruction adherence — did the output match the requested brand identity
- Internal consistency across page titles, meta descriptions, and Open Graph tags
- Unsupported claims and discontinued product references in global metadata
- Structured data (JSON-LD) schema type correctness
- Social sharing preview accuracy on LinkedIn, Facebook, and Slack
The Problem
AI-assisted output had introduced incorrect brand metadata, discontinued product names, and stale SEO terms into page titles, social previews, and structured data across the site.
What I Found
Page titles, meta descriptions, and social card images still referenced a coffee company identity and discontinued product keywords. Structured data identified the site as a coffee organization rather than a professional portfolio. Canonical URLs pointed to the wrong domain. Twitter card metadata referenced a discontinued social handle.
What I Changed
Corrected all metadata to reflect the intended professional identity. Removed discontinued product terminology from global metadata. Replaced the social card image with a portfolio-specific card. Updated structured data from Organization (coffee company) to Person + WebSite + WebPage schema. Corrected canonical URLs and Open Graph tags.
Verification
Independently inspected the rendered DOM source after correction — not just the React component output, but the actual meta tags in document.head. Verified that no coffee-related terms remained in global title, description, OG tags, or Twitter card fields. Confirmed social preview would display the correct professional branding.
Skills Demonstrated
AI output validationMetadata QASEO QAStructured data validationConsistency checkingHallucination detectionSource-to-output verificationSocial preview QA
Selected Interface / Project Gallery
The finished application
Real screenshots of the live project. Select any image to open it full-size.
Outcome
What this project demonstrates
Torch Republic demonstrates my ability to move from an ambiguous business concept to a functioning digital product. It combines product thinking, brand strategy, AI-assisted development, API-connected commerce, content quality, technical troubleshooting, and systematic QA in one live application.
AI-Assisted Product DevelopmentE-commerce Application DesignAPI IntegrationFunctional QAContent EvaluationCustomer Journey TestingPrompt IterationTechnical DocumentationConversion-Focused UXIssue Investigation
Core Capability Frameworks
Where AI Quality Meets Commerce
My work combines human judgment, structured testing, business context and iterative implementation across six connected areas.
01
AI Output Quality & Evaluation
- Evaluating outputs
- Accuracy review
- Consistency
- Guideline adherence
- Identifying errors
- Hallucination awareness
- Edge-case thinking
- Human review
- User-impact evaluation
AI output is only valuable when someone validates whether it is accurate, useful and appropriate for the user.
Professional Process
How I work through AI and digital products
I approach AI and digital products as iterative systems: understand the intended outcome, test the experience, identify failure points, implement improvements, validate the result and continue measuring.
- Observe
- Test
- Identify
- Iterate
- Validate
- Measure
This process connects the AI QA side of my work with the e-commerce implementation side — the same loop applies whether I am evaluating a model output or a checkout flow.
Professional Background
Experience That Supports the Work
My background combines AI output evaluation, document quality, e-commerce implementation, legal support, and published authorship. I use that experience to test systems carefully, identify inconsistencies, improve workflows, and turn complex information into clear next steps.
AI Quality Review & Content Evaluation
Prompt evaluation, instruction adherence, content review, output validation, and QA documentation. Experienced in identifying hallucinations, unsupported claims, factual errors, and consistency issues across AI-generated content. Currently part of the Alignerr network by Labelbox, a global network of experts who actively research and shape the future of AI.
AI & E-Commerce Product Development
Built and tested Torch Republic’s e-commerce application end-to-end: a custom Base44/React storefront integrated with Shopify for live product data, cart behavior, and secure checkout — with AI-assisted development validated by manual QA across customer journeys, responsive behavior, analytics, and content systems.
Document Quality & Legal Support
Professional experience in document review, legal research, consistency checking, structured documentation, and detail-sensitive work requiring accuracy and careful source verification.
Published Author
Long-form research, editing, structural consistency, audience analysis, publishing workflows, and content quality. Experience translating complex topics into clear, accessible material.
Skills & Tools
What I work with
AI Quality
- Prompt testing
- Output validation
- Instruction adherence
- Hallucination detection
- Content evaluation
- Consistency QA
- Research verification
Product & E-Commerce
- Customer journey testing
- Conversion QA
- E-commerce prototyping
- SEO
- Analytics
- Accessibility
- Checkout-flow testing
- Content systems
Development / Technical
- React
- JavaScript
- APIs
- Base44
- Shopify
- GA4
- Google Search Console
QA & Documentation
- Defect documentation
- Regression testing
- Reproducible observations
- Before/after comparison
- Prioritization
- Troubleshooting
Technologies listed reflect practical working knowledge gained through hands-on project work. I distinguish between QA and evaluation expertise (where I am strongest) and working knowledge of development tools (which I use effectively for testing and implementation, not as a senior engineer).
Current Learning
AI Training & Professional Development
Structured training supporting my hands-on work in AI quality, generative AI, responsible evaluation, agentic workflows, and AI-enabled product implementation. Status is shown clearly to distinguish active coursework and exam preparation from completed credentials.
IBM SkillsBuildIn progress
AI Fundamentals
Core AI concepts, machine learning foundations, ethics, and applied AI
GoogleIn progress
AI Essentials
Prompting, responsible AI, productivity workflows, and practical AI use
Amazon Web ServicesExam preparation
AWS Certified AI Practitioner
AI/ML concepts, generative AI, foundation models, responsible AI, and AWS AI services
DeepLearning.AIIn progress
Agentic AI with Andrew Ng
Agentic workflows, reflection, tool use, planning, and multi-agent patterns
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Complete employment history, education, credentials, and references are available upon request to verified employers and recruiters.
Contact
Looking for someone who catches issues before they reach users?
I am interested in roles involving AI quality assurance, AI content evaluation, prompt testing, e-commerce QA, AI implementation, and digital product quality. Let’s talk about how I can help your team ship better output.