Ever wonder why a huge AI model can sound like a top expert one moment, and then confidently make things up the next?
In this seminar, we stop treating AI like a mysterious black box and look directly at how it actually works. We break down, step by step, the logic and math behind AI agents, and why hallucinations happen.
Key Highlights:
Hallucination: We analyze a real-world agent log to pinpoint exactly where things go wrong. Is it a RAG failure? A tool-calling error? Or a fundamental LLM logic gap?
Mitigation: Learn to implement and calculate mitigation strategies by hand, including:
Self-Consistency Checks: Why asking the same question thrice catches a lie.
Tool Registry Verification: Ensuring your agent doesn’t “invent” APIs that don’t exist.
LLM-as-a-Judge: Using high-reasoning models to audit agentic behavior.
Evolution: A visual breakdown of how we moved from basic LLMs to RAG, and finally to the multi-step Agents of today.
Deep-Dive Math: We peel back the transformer architecture to look at Softmax layers and attention mechanisms. You’ll see exactly how “Sink Tokens” can prevent models from forcing a wrong answer when no right answer exists.
Here’s a preview of the material I prepared for this seminar:
Special thanks to Ofer Mendelevitch and Vectara for hosting this seminar!
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Recording & Workbook
Members: the full recording and the Excel workbook are below ↓