In the race to harness the power of generative AI, organizations are discovering a hidden challenge: precision. Models are only as effective as the data they access, yet most approaches to Retrieval-Augmented Generation (RAG) lack the dedicated, fine-tuned pipelines needed to ensure the right information is delivered at the right time.
Today, most RAG systems pull from vast, generalized data lakes, leading to noisy outputs and frustrating inefficiencies. The result? Wasted resources, inconsistent responses, and missed opportunities for real-time decision-making. But what if you could create an AI system that doesn’t just retrieve data—but understands its context, delivering precise, actionable insights in milliseconds?
This is where agenticRAG comes into play—a breakthrough in AI architecture that pairs dedicated retrieval pipelines with intelligent agents to deliver pinpoint accuracy. By segmenting your data storage and retrieval processes specifically for training vs. inference, you can achieve hyper-focused precision while dramatically reducing latency and costs.
Imagine an AI system that knows exactly what data it needs and how to get it with zero lag—a system that’s tuned to perform like a well-trained expert in your domain.
Curious to discover how you can optimize your AI applications for laser-focused accuracy? Join me as to learn more about AgenticRAG and fine tuning your models.
Speaker
Adi Polak
Director, Advocacy and Developer Experience Engineering @Confluent
Adi is an experienced Software Engineer and people manager. She has worked with data and machine learning for operations and analytics for over a decade. As a data practitioner, she developed algorithms to solve real-world problems using machine learning techniques while leveraging expertise in distributed large-scale systems to build machine learning and data streaming pipelines. As a manager, Adi builds high-performance teams focused on trust, excellence, and ownership.
Adi has taught thousands of students how to scale machine learning systems and is the author of the successful book Scaling Machine Learning with Spark and High Performance Spark 2nd edition.
