Computational Intelligence Society and co-sponsoring IEEE Boston AI Group

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:

• Structural Document Preprocessing & Chunking: Parsing hierarchical documents into semantically coherent units rather than arbitrary fixed-character splits.
• Hybrid Search & Retrieval: Combining sparse keyword matching (BM25) with dense semantic embeddings to capture both exact terminology and conceptual intent.
• Adaptive Feedback Learning: Integrating dynamic feedback for improving retrieval.
• Sentence-Level In-Line Citations: Adding verifiable citation tags, so every fact links back to its source document.
• Cross-Session Persistence: Extracting and storing high-signal user profile facts to maintain continuity across distinct user sessions.

Nutan Sahoo of Microsoft Corp.

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.

Advitya Gemawat is a Software Engineer 2 at Microsoft

Location:  Microsoft NERD Center, Kendall Square, 1 Memorial Drive, Cambridge, Massachusetts

Room Number: NERD/1250, Abigail Adams

Registration