About the session
Most Generative AI discussions focus on models and prompting, but real-world AI systems are much more than a single LLM call. Production-grade AI applications require orchestration, routing, retrieval, memory, guardrails, observability, evaluation, and cost optimization to deliver reliable business value at scale.
In this hands-on session, we will break down the complete architecture of a modern AI application—from user query to final response. Participants will learn how different layers such as agent orchestration, multi-model routing, Retrieval-Augmented Generation (RAG), tool calling, memory management, safety guardrails, evaluation frameworks, and monitoring work together in a production environment.
The session will go beyond theory with a live end-to-end implementation of an AI application. We will build a working system that intelligently routes requests across multiple LLMs, leverages external tools and knowledge sources, applies safety checks, tracks observability metrics, and evaluates response quality. Attendees will gain practical insights into the design patterns used by leading AI products and learn how to move from notebooks and demos to scalable, enterprise-ready AI solutions.
Session Takeaways
Understand the architecture of production AI systems
Learn when and why to use multiple LLMs
Build intelligent routing and orchestration workflows
Implement RAG, memory, and tool calling
Add guardrails, safety checks, and governance controls
Measure quality using evaluation frameworks
Monitor AI applications with observability tools
Deploy AI systems using industry best practices
