RAG

Overview A cross-platform mobile application that provides real-time lecture transcription, AI-powered summaries, and intelligent Q&A to enhance learning efficiency. Key Achievements 30% improvement in lecture review efficiency during pilot tests with 50+ students 41% higher engagement and 2× faster response times through A/B testing on note layouts and Q&A interface Fault-tolerant Whisper + LLM pipeline for reliable real-time processing Technical Implementation Architecture Frontend: React Native for cross-platform compatibility Backend: Flask with Python Celery for distributed task processing AI Pipeline: Whisper for speech-to-text, LLM for summarization and Q&A Infrastructure: Azure cloud services for scalability Design: Figma for UI/UX design and prototyping Key Features Real-time transcription using advanced speech recognition AI-generated summaries with key points and concepts Intelligent Q&A system for lecture content A/B testing framework for continuous improvement Fault-tolerant processing for reliable performance Results 30% efficiency gain in lecture review during pilot tests 41% higher engagement through optimized user interface 2× faster response times for Q&A interactions Positive feedback from 50+ beta users across multiple institutions View on GitHub →

Overview A retrieval-augmented generation (RAG) system designed to index and query large textbook collections for intelligent question-answering. Key Achievements Indexed 1,000+ textbook pages into FAISS with dense + BM25 retrieval Implemented DocETL style query planning for efficient document processing Built ensemble re-ranking system for improved answer quality Added structured logging and visualization for system monitoring Technical Implementation The system combines multiple advanced techniques: Dense Retrieval: FAISS indexing with sentence transformers Sparse Retrieval: BM25 for keyword-based matching Query Planning: DocETL style processing for complex queries Re-ranking: Ensemble methods for answer quality improvement Inference: Support for llama.cpp inference and document chunking Impact This RAG pipeline enables efficient querying of large document collections, making educational content more accessible and searchable through natural language interfaces. ...