Most engineering teams pick their search method based on what they already know. They reach for BM25 because it ships with OpenSearch. They bolt on an LLM because the PM asked for "AI search." Nobody benchmarked it.
Three years later, the RAG pipeline is confidently returning wrong answers — while a graph-based approach returns exact, explainable results in 340ms.

This talk is a decision framework, not a feature tour.

We run one real query, "Find me an engineer, ex-Googler, now running a Series B fintech" — through five methods: BM25, semantic, hybrid, RAG, and GraphRAG. Each gets closer. Each exposes a new failure mode. The audience watches hybrid search outperform BM25 by 14% on relevance (AWS/OpenSearch benchmark, 2024), sees RAG hallucinate a funding stage despite correct retrieval (5–15% real-world rate, K2view 2024), and watches GraphRAG traverse a knowledge graph to return a verified, fully-explained answer.

The talk closes with a decision matrix => query complexity × data structure × latency — that maps directly to OpenSearch implementation paths available today in 2.10+.
Every claim is sourced. Slides, demo app, and decision matrix.