Danya Tazi Mokha is an AI and Data Engineer at CIH Bank, where she automates data pipelines and develops LLM driven tools that support both customers and employees. After a six-month internship, she joined the team full time to build solutions that streamline workflows and optimize internal processes.
A recent graduate of Al Akhawayn University with a BSc in Computer Science and a minor in Business Administration, Danya combines technical skill with business understanding to turn AI ideas into practical solutions.
This session presents a side by side comparison of two production RAG implementations solving different problems. One uses classic vector search for internal troubleshooting documentation. The other employs hierarchical LLM routing for structured product catalogs.
The contrast reveals critical questions: Is your content naturally unstructured or does it have implicit hierarchy? Do users ask "how to" questions or "which one" questions? Is semantic similarity what you actually need, or do you need categorical precision and exact matching?
You'll discover both architectures, understand why one retrieval strategy can't serve all content types, and leave with a decision framework for your own systems. Because sometimes the "advanced" architecture is overengineering, and the "simple" one is naive.
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