Large Language Models (LLMs) are powerful, but they often hallucinate when accuracy is critical. Retrieval Augmented Generation (RAG) is emerging as the leading technique to ground LLM responses in reliable, domain-specific knowledge.

In this workshop, I’ll guide participants through the fundamentals of RAG (vector RAG & graph RAG); I'll demonstrate real-world implementations; and show optimisation techniques to improve response accuracy, consistency, and trustworthiness (Reranking, Caching, & RAG System Evaluation). Drawing from my experience building AI Teaching Assistants at Zindua School, I’ll extend these lessons to high-stakes fields like Education, Medicine, and Law – where precision matters most.