Build Accurate, Custom Agentic RAG Systems Locally with HP AI Studio
Want to develop more capable RAG (Retrieval-Augmented Generation) systems using local models? On June 27th, join HP AI Engineer Ata Turhan for a live webinar showcasing how to streamline the development of advanced agentic workflows using HP AI Studio.
In this session, Ata will walk through a real-world example of a local Agentic RAG pipeline that combines TRT-LLM and LangGraph to answer platform-related questions by retrieving information from Zdocs—a curated collection of documents about HP AI Studio. The workflow leverages query rewriting, memory, and tool use to generate highly accurate, reference-backed responses.
You’ll get a hands-on look at how HP AI Studio makes it easy to prototype, customize, and deploy advanced AI systems directly on local hardware.
What You’ll Learn:
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How to architect a local Agentic RAG system using HP AI Studio
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Techniques to improve answer quality with query rewriting, memory, and tool calling
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How to ground AI responses in content from multiple document sources
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Best practices for building and testing agentic workflows in a local environment
Bonus: Want to dive in ahead of time? You can check out the project files on GitHub before the session.
