AI-Powered QA Platform
Built an automated testing pipeline that uses LLMs to generate test cases from user stories, execute them against staging environments, and report findings with suggested fixes.
The Challenge
Generating contextually relevant test cases without hallucinating non-existent UI elements. The system needed to understand the actual application structure.
The Solution
Built a RAG pipeline grounded in the app's component tree and design system docs
Implemented code-aware indexing extracting component props, routes, and state shapes
Created multi-stage pipeline: context retrieval → test outline → full test → validation
Added feedback loop where failed tests refine the model's understanding
Built With
Impact
Reduced QA cycle time by 60% while increasing test coverage 3x. Bug detection rate improved to 85% on first pass.