AI and ML are revolutionizing search relevance by moving beyond keyword matching to semantic understanding. Dense embedding models and cross-encoder rerankers capture meaning, not tokens, while vector search and ANN algorithms surface conceptually relevant results even with zero term overlap. This is transformative in e-commerce, healthcare, and enterprise search where vocabulary mismatch is common.

Learning to Rank (LTR) trains ML models on click-through data and behavioral signals to learn adaptive ranking functions that outperform static rules. Combined with contextual features, LTR delivers personalized relevance at scale.

LLMs and RAG architectures blend search precision with generative reasoning, enabling conversational search, query reformulation, and intelligent answer synthesis. Challenges like model interpretability, bias, and latency drive innovation in distillation, quantization, and hybrid pipelines.

In this session, we cover embedding strategies, hybrid retrieval, LTR-based relevance tuning, RAG patterns, and evaluation frameworks using OpenSearch. Attendees will leave with actionable techniques to build search experiences that truly understand user intent.