Improve accuracy of LLM using TAG and RAG

Course Overview: Large Language Models (LLMs) like GPT-4 are powerful but prone to errors such as hallucination and lack of domain context. This course tackles these challenges through Taxonomy-Augmented Generation (TAG) and Retrieval-Augmented Generation (RAG), two complementary methods that improve accuracy and reliability. Objectives: Participants will learn to apply supervised classification using taxonomies, build custom embeddings, use vector databases, fine-tune LLMs with real-world data, and complete hands-on exercises. The capstone project integrates TAG and RAG into a practical use case. Prerequisites: Foundational knowledge of prompt engineering, basic computer literacy, and an analytical mindset are expected. Prior exposure to ChatGPT or equivalent tools is beneficial. Audience: AI professionals, data scientists, and developers seeking advanced generative AI skills. Target skills include designing robust AI solutions and enhancing LLM accuracy for domain-specific scenarios. Key Learning: * TAG provides structured, taxonomy-based classification to reduce errors. * RAG retrieves relevant external data to ground responses. * Combined, they create adaptive, context-aware AI systems. Outcomes: Learners gain the ability to design, test, and deploy advanced generative AI solutions, showcase mastery through a capstone project, and earn a Credly certification recognizing expertise in LLM accuracy and integration. This program bridges theory and practice, preparing participants to deploy reliable, domain-specific AI systems across industries such as healthcare, finance, and education.