Semantic AI: Language and Embedding Models hand-in-hand

Generative AI has reshaped how we build intelligent systems, but true AI reliability goes beyond text generation. Semantic AI shifts the focus from mere language generation to deep understanding and structured decision-making, where language models and embedding models work together to power robust applications.
SLMs & LLMs process and generate natural language, enabling structured outputs for APIs and interactive applications. Embedding models, on the other hand, capture semantic relationships within data, enhancing query filtering, task routing, and retrieval accuracy. Together, they create AI systems that are more context-aware, scalable, and precise.
Christian explores how structured outputs from LMs and embedding-driven semantics can work together to improve an AI system's performance. Through a real-world example, he demonstrates how query validation, task routing, and API integration create robust AI-powered interactions.