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UID:pretalx-pyconde-pydata-2025-JABVHK@pretalx.com
DTSTART;TZID=CET:20250425T113500
DTEND;TZID=CET:20250425T120500
DESCRIPTION:Retrieval Augmented Generation (RAG) has become a cornerstone i
 n enriching GenAI outputs with external data\, yet traditional frameworks 
 struggle with challenges like data noise\, domain specialization\, and sca
 lability. 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\, d
 elivering a precision-focused\, scalable\, and interpretable solution. By 
 leveraging the structured context of knowledge graphs\, Fast GraphRAG enha
 nces data adaptability\, handles dynamic datasets efficiently\, and provid
 es traceable\, explainable outputs while InstructLab adds domain depth to 
 the LLM through Fine-tuning. Designed for real-world applications\, it bri
 dges the gap between raw data and actionable insights\, redefining intelli
 gent retrieval for developers\, researchers\, and enterprises. This talk w
 ill 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.
DTSTAMP:20260410T235402Z
LOCATION:Platinum3
SUMMARY:Enhancing RAG with Fast GraphRAG and InstructLab: A Scalable\, Inte
 rpretable\, and Efficient Framework - Tuhin Sharma
URL:https://pretalx.com/pyconde-pydata-2025/talk/JABVHK/
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