Large Language Models (LLMs) get all the attention, but Embedding Models might be the real unsung heroes of AI systems. Christian shows how embedding-driven semantics can guard against hallucinations, dynamically route tasks, and supercharge Retrieval-Augmented Generation (RAG) workflows. Combined with Small or Large Language Models, embeddings offer a scalable way to build AI systems that aim to be more accurate, efficient, and context-aware. If you're ready to move beyond LLM hype, discover a semantic-centric approach in a pragmatic way.