AI Engineer World's Fair 2026 — unofficial import demo

If you build RAG, you reached for vector search first. This lab is about everything that happens after you realize embeddings alone don't cut it in production. You'll write real queries — semantic, lexical, and hybrid — feel exactly where each one fails, and walk out with a production-grade retrieval pipeline and the judgment to know which technique to reach for when.

What you'll actually do:
1. Dense vector search, and the mechanism behind it. Run semantic queries over a  semantic_text  field backed by Jina v5 embeddings — generated server-side, at query time, by the Elastic Inference Service (EIS). No embedding service to stand up, no client-side inference code. We open the hood on how query-time embedding actually works.
2. Break it. Throw adversarial queries at pure vector — exact error codes, version numbers (8.18 vs 9.0), precise config keys — and watch semantic similarity blur the exact match you needed. Then bring in BM25 lexical search to rescue it… and find the queries where keyword search whiffs. Each method is strongest exactly where the other is weakest.
3. Hybrid, properly. Fuse lexical + semantic with Elasticsearch retrievers. Learn the two fusion strategies that matter — Reciprocal Rank Fusion (RRF) and linear combination with score normalization — when to use each, and how to tune them. Optional: cross-encoder reranking with Jina Reranker v2.
4. Why this is the whole game for agents. Wire the hybrid retriever into a RAG flow and prove that retrieval quality, not the model, determines answer quality. Only synthesis truly needs the LLM - retrieve, rank, filter, and document-level security are database work done in milliseconds for a fraction of the cost. The contrarian takeaway: most of your RAG pipeline shouldn't be LLM calls at all.