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.
ai-powered-qa-platform.app
60%
Faster QA Cycles
3x
More Test Coverage
85%
Bug Detection Rate
< 5min
Per Test Suite
The Challenge
Generating contextually relevant test cases without hallucinating non-existent UI elements. The system needed to understand the actual application structure.
The Solution
1
Built a RAG pipeline grounded in the app's component tree and design system docs
2
Implemented code-aware indexing extracting component props, routes, and state shapes
3
Created multi-stage pipeline: context retrieval → test outline → full test → validation
4
Added feedback loop where failed tests refine the model's understanding
// Tech Stack
Built With
Next.js
OpenAI GPT-4
LangChain
MongoDB
Docker
Redis
Playwright
TypeScript
// Results
Impact
Reduced QA cycle time by 60% while increasing test coverage 3x. Bug detection rate improved to 85% on first pass.