AI-powered generation of targeted learning and assessment content from complex source material.
← Back to ProjectsThis project explores how large language models and retrieval systems can be used to generate targeted learning and assessment content from complex documents. Using a Retrieval-Augmented Generation (RAG) pipeline, the system processes dense source material and produces structured, context-aware multiple-choice questions with supporting references.
While initially designed for regulatory content, the same approach can be applied across a wide range of use cases—such as employee training, policy reinforcement, onboarding, or knowledge validation in any domain where accurate interpretation of written material is critical.
Organizations often rely on dense written materials—policies, procedures, technical documentation—that are difficult to absorb and retain. Traditional training approaches are static, time-consuming to create, and rarely tailored to specific roles or knowledge gaps.
I built a Retrieval-Augmented Generation (RAG) pipeline that combines document retrieval with large language models to generate structured, context-aware questions directly from source material. The system preserves document structure, retrieves relevant context, and produces verifiable outputs with supporting references.
The result is a scalable framework for generating targeted learning and assessment content from complex documents. While demonstrated on regulatory material, the same approach can be applied to employee training, onboarding, policy reinforcement, or knowledge validation across industries.