Boost LLM Accuracy: Build Reliable RAG Apps
As enterprises increasingly adopt Retrieval Augmented Generation (RAG) for their LLM applications, the need for robust AI observability and performance monitoring becomes critical. Join DataStax and Fiddler AI for an insightful session on implementing a comprehensive observability stack for RAG applications that ensures accuracy, safety, and reliability at scale.
We'll explore how the combination of DataStax's AI Platform and Fiddler's AI Observability stack creates a powerful foundation for production-grade RAG applications. Learn how to monitor key metrics including hallucinations, toxicity, PII leakage, and answer relevance in real-time, while leveraging Astra DB's vector search capabilities to maintain high performance at scale.
In this session you’ll learn:
- Best practices for implementing RAG observability in production environments
- Real-time monitoring of LLM application performance using Fiddler's Trust Models
- Leveraging Astra DB's vector capabilities for accurate and scalable information retrieval
- Strategies for detecting and addressing common RAG challenges such as hallucinations and data drift
- Practical demonstrations using real-world use cases and applications
Whether you're building customer-facing chatbots, internal knowledge bases, or complex document analysis systems, this session will provide valuable insights into maintaining and optimizing your RAG applications for enterprise use. Join us to learn how to build more reliable, transparent, and trustworthy AI applications.
Can’t join us live? Register anyway and we’ll share the replay afterward!