Building Robust RAG Systems

In this hands-on technical session from the Artificial Unintelligence Conference 2025, Pavan Belagatti (GenAI Evangelist and Developer Advocate, SingleStore) breaks down how to design and deploy reliable Retrieval-Augmented Generation (RAG) systems that deliver accuracy, speed, and trustworthiness at scale.

Pavan walks through the architecture of modern RAG pipelines — showing how frameworks like LangChain tie together model logic, vector databases, and external knowledge retrieval. He also demonstrates how SingleStore enables fast semantic search and real-time retrieval, while models like DeepSeek Carbon provide flexible reasoning capabilities.

The talk goes beyond code samples to focus on engineering maturity — covering evaluation, observability, and modular design as critical ingredients for production-ready AI systems.

Key themes include:

The fundamentals of Retrieval-Augmented Generation (RAG)

Using LangChain to orchestrate AI workflows

How SingleStore powers fast and accurate context retrieval

Choosing and evaluating models like DeepSeek Carbon and LLaMA

Designing modular, observable RAG pipelines for production use