Retrieval Augmented Generation (RAG) has become a cornerstone in enriching GenAI outputs with external data, yet traditional frameworks struggle with challenges like data noise, domain specialization, and scalability. In this talk, Tuhin will dive into open-source frameworks Fast GraphRAG and InstructLab, which addresses these limitations by combining knowledge graphs with the classical PageRank algorithm and Fine-tuning, delivering a precision-focused, scalable, and interpretable solution. By leveraging the structured context of knowledge graphs, Fast GraphRAG enhances data adaptability, handles dynamic datasets efficiently, and provides traceable, explainable outputs while InstructLab adds domain depth to the LLM through Fine-tuning. Designed for real-world applications, it bridges the gap between raw data and actionable insights, redefining intelligent retrieval for developers, researchers, and enterprises. This talk will showcase Fast GraphRAG’s transformative features coupled with domain specific Fine-tuning leveraging InstructLab and demonstrate its potential to elevate RAG’s capabilities in handling the evolving demands of large language models (LLMs) for developers, researchers, and businesses.