Building a Production-Grade Retrieval Augmented Generation (RAG) Pipeline
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Description: Most standard RAG tutorials rely on a naive pattern: splitting text, embedding chunks, and injecting the top matches directly into an LLM prompt. While simple to implement, this approach frequently fails in real-world scenarios, retrieving semantically related but factually irrelevant context, generating unverified assertions, etc. In this hands-on workshop, we move beyond naive RAG to build a robust, production-ready RAG pipeline. Participants will learn how to design resilient architectures that handle complex, domain-specific document. Key Topics & Implementations Covered:
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Date and Time
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- Microsoft NERD Center, Kendall Square
- 1 Memorial Drive
- Cambridge, Massachusetts
- United States 02142
- Room Number: NERD/1250, Abigail Adams
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- Co-sponsored by IEEE Boston AI Group
Speakers
Nutan Sahoo of Microsoft Corp.
Building a Production-Grade Retrieval Augmented Generation (RAG) Pipeline
Biography:
Nutan Sahoo is an Applied AI Scientist II at Microsoft, where she develops and deploys production-scale machine learning and generative AI solutions for enterprise applications. Her current work focuses on building RAG and agent-based AI systems to help on-call engineers resolve incidents faster, while also developing scalable evaluation pipelines to improve the performance and reliability of these generative AI systems.
She holds a master’s degree in Health Data Science from Harvard University. And her experience spans multiple industries, where she has applied machine learning to real-world problems at organizations such as Brigham and Women's Hospital, Children's Hospital of Philadelphia, Meta, and Microsoft.
Beyond her industry work, Nutan is actively involved in the AI research community, serving as a technical reviewer for leading AI conferences and participating in technical talks and community initiatives focused on generative AI and applied machine learning. Her recent research on high-fidelity synthetic ECG generation—conducted in collaboration with Mass General Brigham—was presented at and received the Best Paper Award at the Generative AI for Health Workshop at NeurIPS 2025.
Advitya Gemawat of Microsoft Corp.
Building a Production-Grade Retrieval Augmented Generation (RAG) Pipeline
Biography:
Advitya is an ML Engineer at Microsoft, specializing in scalable machine learning systems and Responsible AI (RAI). He has authored publications and received awards from venues such as VLDB, ACM SIGMOD, and CIDR.
At Microsoft, Advitya has worked with Azure Edge & Platform, Gray Systems Lab, and Windows, building ML and LLM services to enhance developer productivity. He also developed Azure ML's RAI tooling for computer vision models and Azure OpenAI Evaluations, released at Microsoft Build (2023-2025).
Currently, Advitya leads LLM fine-tuning efforts to accelerate merge conflict resolution in the planet’s largest git codebase (the Windows OS repo), and is building Agents to accelerate Windows’ monthly Software Updates. He also served as a technical reviewer of the Amazon bestseller 'Ace the Data Science Interview' and was recognized as a "25 under 25: Top Data Science Contributor & Thought Leader."
Presented by: Nutan Sahoo, an Applied Scientist 2 at Microsoft and Advitya Gemawat, a Software Engineer 2 at Microsoft.
Keywords: Artificial Intelligence, Machine Learning, Natural Language Processing, Information Retrieval, Adaptive Information Retrieval, Feedback, Workshop.