Organizations globally face a critical bottleneck in generative AI and knowledge management: traditional keyword search fails to capture semantic meaning, leading to irrelevant retrieval, failed RAG (Retrieval-Augmented Generation) architectures, and high computational waste. Overcoming the vocabulary mismatch problem requires modernising underlying retrieval infrastructure.
This course provides a comprehensive architectural briefing on vector embeddings and enterprise semantic search. Participants will systematically explore how transformer-based embedding models convert natural language into multi-dimensional meaning spaces. The curriculum examines the complete lifecycle of semantic retrieval, from contrastive learning and model selection using public benchmarks to production deployment involving vector databases, dimensionality reduction, and sophisticated hybrid search techniques.
