Retrieval Augmented Generation Overview

A technique that combines language models with external knowledge sources to provide more accurate and informative responses.

A structured database that represents entities, relationships, and properties of data, organized into categories and subcategories for better organization and retrieval.

A type of artificial intelligence that can independently set and achieve goals through self-prompting, learning, and adaptation.

A smaller version of a large language model with fewer parameters, requiring less processing power and faster training, suited for localized applications.

Embedding data in RAG transforms text into numerical representations, allowing the model to easily compare and identify relationships between different data points.

A knowledge graph provides a structured framework for classifying topics, subcategories, and relationships between concepts, enabling the creation of more specific and targeted quizzes.

Agentic AI can act as personalized tutors, providing customized feedback, answering complex questions, and even generating personalized learning materials.

SLMs offer a balance between efficiency and effectiveness, allowing for rapid training and deployment while still providing high-quality quiz generation, particularly for localized applications.

By mimicking the format of actual exams, these generators allow students to practice answering questions in a realistic environment, improving their test-taking skills and confidence.

RAG allows the generator to access and integrate external knowledge sources, ensuring that generated questions are grounded in accurate and relevant information.

A knowledge graph provides a structured framework for organizing knowledge, ensuring that generated quizzes are logically coherent, contain relevant information, and are appropriate for specific learning objectives.

Agentic AI enables the creation of interactive and adaptive learning systems that can provide personalized feedback, answer questions, and adjust the learning experience based on student needs.

SLMs, despite being efficient, may have limited capabilities when it comes to complex tasks or handling large amounts of data, requiring careful consideration of their limitations.

By integrating RAG and knowledge graphs, the generator can access a broader range of information, create more accurate and diverse questions, and provide a more realistic exam simulation experience for students.