Ever wished your AI could do more than just answer a question?
What if you could teach it how to think, decide, and verify?
This workshop introduces one of the newest and most powerful AI architectures: graph-based reasoning systems, where AI agents follow structured decision graphs to retrieve information, reason step by step, and produce reliable, evidence-based answers.
We begin from first principles, explaining how large language models work and why they cannot be trusted on their own. You will then build a complete RAG pipeline that connects an open-source LLM to real documents, enabling it to retrieve evidence and generate fact-grounded answers instead of confident guesses.
From there, we move beyond linear pipelines.
You will transform your RAG system into a graph-based AI agent: an autonomous assistant whose reasoning is explicit, structured, and controllable. Using a reasoning graph, the AI learns when to retrieve information, how to combine multiple sources, when to verify results, and when to stop. Instead of one-shot responses, your system follows a clear decision flow that mirrors human problem-solving.
By the end of the workshop, you will have built a complete, open-source AI assistant that can read documents, retrieve knowledge, reason through a graph, and
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Ever wished your AI could do more than just answer a question?
What if you could teach it how to think, decide, and verify?
This workshop introduces one of the newest and most powerful AI architectures: graph-based reasoning systems, where AI agents follow structured decision graphs to retrieve information, reason step by step, and produce reliable, evidence-based answers.
We begin from first principles, explaining how large language models work and why they cannot be trusted on their own. You will then build a complete RAG pipeline that connects an open-source LLM to real documents, enabling it to retrieve evidence and generate fact-grounded answers instead of confident guesses.
From there, we move beyond linear pipelines.
You will transform your RAG system into a graph-based AI agent: an autonomous assistant whose reasoning is explicit, structured, and controllable. Using a reasoning graph, the AI learns when to retrieve information, how to combine multiple sources, when to verify results, and when to stop. Instead of one-shot responses, your system follows a clear decision flow that mirrors human problem-solving.
By the end of the workshop, you will have built a complete, open-source AI assistant that can read documents, retrieve knowledge, reason through a graph, and answer with evidence.
Not a simple chatbot.
A truly intelligent AI agent that follows a path of thought.
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