Build Accurate, Custom Agentic RAG Systems Locally with HP AI Studio

Want to build more capable RAG systems using local models? Join us to explore how HP AI Studio can streamline the development of Agentic Retrieval-Augmented Generation (RAG) pipelines enhanced with query rewriting, memory, and tool calling. Ata Turhan, an AI Engineer at HP, will walk through a real-world example of a local agentic workflow in HP AI Studio that uses TRT-LLM and LangGraph to answer questions about the platform by retrieving information from Zdocs, a collection of documents about HP AI Studio, and generating accurate, reference-backed responses.

Throughout the session, you’ll see how HP AI Studio enables rapid prototyping, customization, and deployment of advanced AI systems on local hardware.