Aarti Jha
Aarti Jha is a Principal Data Scientist at Red Hat, where she leads the development of AI-driven solutions that streamline internal operations and reduce costs. She has more than seven years of experience designing and deploying machine learning and generative AI solutions across multiple industries. A frequent speaker at developer and data science conferences, she shares practical insights on applied AI, LLMs, and building AI systems that deliver measurable business value.
Sessions
The quality of the retrieval component is what drives Retrieval-Augmented Generation (RAG) systems. Therefore, a well-structured, measurable, and robust retrieval pipeline is critical to building effective large language model (LLM) applications.
Working through guided code examples and hands-on experimentation, attendees will collectively develop, optimize, and enhance the performance of a complete RAG pipeline by improving retrieval in three stages: Pre-Retrieval, Mid-Retrieval, and Post-Retrieval. We will also cover structured and multimodal document parsing with Docling, systematic evaluation with RAGAS, and a capstone Agentic RAG demo using LangGraph. The toolkit integrates Qdrant for vector search and the LangChain ecosystem for orchestration and experimentation.
During the hands-on session, attendees will use Jupyter notebooks to learn about, experiment with, and benchmark techniques that produce significant improvements to retrieval quality using production-ready open-source libraries. At the end of the session, each participant will be equipped with a reusable “Retrieval Playground” framework that can be leveraged to design, evaluate, and continuously improve RAG systems across various application domains.
Installation Instructions: https://github.com/mahimaarora/retrieval-playground/tree/main/setup-guides
Scientific breakthroughs don’t happen in plain text, they live inside multi-column research papers, dense data tables, and intricate simulation diagrams. Yet the moment standard AI and Retrieval-Augmented Generation (RAG) pipelines encounter these layouts, they fail. Tables are flattened into meaningless strings. Figures are ignored. The structural signals that drive scientific reasoning disappear.
In this talk, we show how to rescue scientific knowledge from the “text-flattening” trap using Docling, an open-source document understanding library designed to preserve layout, hierarchy, and element boundaries. Instead of reducing everything to text, we treat tables, figures, and sections as first-class data structures. Attendees will experience a live demo of a realistic scientific R&D workflow: uploading multiple dense technical PDFs, executing cross-document natural language queries, and successfully retrieving synthesized insights from text, structured tables, and scientific images