Document Insight Assistant — RAG-Based AI Application developer
Developed a web-based AI application enabling users to upload and query documents for context-grounded answers. Built and optimized a Retrieval-Augmented Generation (RAG) pipeline with document ingestion, chunking, semantic retrieval, and response generation. Iterated multiple versions using a local LLM for document-based QA and text summarization to provide reliable results to end users. • Implemented document chunking and semantic retrieval to optimize context relevance. • Designed AI workflows to perform extractive and abstractive question answering over unstructured texts. • Evaluated the AI system’s outputs for accuracy and utility in information retrieval tasks. • Applied prompt engineering and RAG strategies to improve answer quality and summarization consistency.