Enterprise Retrieval-Augmented Generation (RAG) systems are being deployed rapidly, but many are failing to deliver consistent, reliable outputs when exposed to real-world data environments. What begins as a straightforward retrieval pipeline often becomes unstable as data quality issues, poor chunking strategies, weak embedding alignment, and inefficient indexing approaches compound. As system complexity increases, retrieval precision degrades, latency rises, and outputs become less grounded - creating significant challenges for engineering and architecture teams responsible for production performance.
As these challenges surface, organizations are moving toward more rigorous approaches to designing, tuning, and validating RAG systems - placing greater emphasis on data preprocessing pipelines, retrieval optimization, indexing strategies, and evaluation methodologies. This includes refining how data is chunked and embedded, how vector search is tuned, and how retrieval outputs are validated and monitored, enabling more deterministic, scalable, and high-performing systems in production environments.
Please Note - this is not a passive event. At the end of the speakers’ presentation, all attendees are expected to actively participate in the discussion and contribute real-world challenges and solutions.
Topics of discussion will include, yet will not be limited to:
- How data ingestion and preprocessing pipelines are being structured, including chunking strategies, metadata enrichment, and embedding model selection
- Where retrieval pipelines break down at scale, including issues with vector similarity, context dilution, and irrelevant document surfacing - and how these are being addressed
- How indexing and vector database strategies are being optimized for performance, including hybrid search approaches, re-ranking techniques, and latency considerations
- The evaluation and monitoring frameworks being implemented to measure retrieval quality, grounding accuracy, and system performance over time
Chirag Shah, Global Chief Information Security Officer & Data Protection Officer, MODEL N
Deep Patel, Senior Data Engineering Lead, ROBINHOOD
Gladson Baby, Vice President & Director of Data & AI Enablement, Intelligent Automation & System Integration, FIFTH THIRD BANK
Pradeep Madhavankutty, Director, AI Delivery & Product Development, STANDARD CHARTERED
Moderator: Jacqueline Starr, IT Business Analysis Senior Specialist, CNA INSURANCE
