Cover image for The Solution:
I am building Corex

The Solution: I am building Corex AI, a mobile application that uses a Retrieval-Augmented Generation (RAG) architecture to ground AI models (OpenAI, Gemini, Claude) directly in localized college data. How it Works: Users upload their specific course materials, such as syllabus notes for CTE 323 or Electronics II, into the "Department Files" module [source: 1]. The system chunks and stores these documents in a vector database for semantic search. When a user queries the "Math Solver" feature, the backend retrieves the precise formula methodologies from their lecturer's notes [source: 1]. The LLM generates a step-by-step calculation—like the Laplace Transform solution showcased in the interface—mirroring the exact grading rubric expected by the department [source: 1].

Chibuzor Osemene

Outcome-driven Full-Stack Systems Architect