TLDR: At OpenSearchCon Europe 2026, Laysa Uchoa from NordCloud reframed generative AI hallucinations as a retrieval problem rather than an LLM limitation. She demonstrated production RAG architectures where hallucination rates dropped by over 80% by prioritizing hybrid retrieval (combining BM25 lexical scoring and k-NN vector search), implementing disciplined chunking strategies, and utilizing rigorous evaluation frameworks over simple “vibes”. |
Based on the OpenSearchCon Europe 2026 talk by Laysa Uchoa | Watch the full session →
If you have shipped a GenAI feature to production, you have probably already met the enemy: the confident, fluent, completely wrong answer. Hallucinations are not a theoretical concern anymore. They are the thing that erodes user trust in a demo, gets flagged in a security review, and quietly kills adoption after launch.
At OpenSearchCon Europe 2026 in Prague, Laysa Uchoa came armed with receipts. Her session, “Vectors vs. Hallucinations: OpenSearch’s GenAI Survival Kit,” made the case that the hallucination problem is not fundamentally a model problem—it is a retrieval problem. And retrieval is something the OpenSearch community knows how to fix.
The retrieval gap
Most teams building GenAI applications reach for an LLM first and figure out retrieval later. That sequencing is exactly backwards. LLMs do not fail because they are bad at reasoning; they fail because they are reasoning over incomplete or irrelevant context. The model is only as good as what you hand it. When the retrieval layer is fuzzy, the generation layer invents.
Uchoa walked through the architecture of production RAG pipelines where hallucination rates dropped by more than 80% once retrieval was treated as a first-class engineering problem rather than an afterthought. The shift came from a few specific decisions: moving from pure vector retrieval to hybrid BM25 + vector fusion, investing seriously in chunking strategy, and building evaluation frameworks that measure recall rather than just vibes.
Hybrid retrieval is not optional
One of the clearest messages from the session was that pure vector search, despite all the excitement around embeddings, is not sufficient for enterprise use cases. Vector search is powerful for semantic similarity, but it can miss exact matches, struggle with rare terms, and produce retrieval results that are semantically adjacent but factually wrong in the specific way your user cares about.
Hybrid retrieval—combining BM25 lexical scoring with k-NN vector search through OpenSearch’s neural ranking and k-NN plugins—consistently outperforms either approach in isolation on real enterprise knowledge bases. The fusion is not complicated to set up, but the tuning matters, and Uchoa shared the practical guidance that most tutorials skip: how to weight the two signals relative to each other depending on your data characteristics and query patterns.
Failure autopsies and what you actually learn from them
What made this talk particularly useful was the honesty about where things go wrong. Uchoa did not just show the happy path. She walked through failure modes: what happens when chunk boundaries fall in the wrong place, how recall degrades as document volume scales into petabyte territory, and what evaluation frameworks can catch problems before they reach production.
The live demos covered RAG over petabyte-scale trace data, chunking optimization techniques that materially change what the model gets to see, and an eval framework for benchmarking recall. The Kubernetes deployment blueprints and code snippets she shared throughout are the kind of concrete, reusable material that does not show up in blog posts but makes talks worth attending.
The practical takeaway
The hallucination problem is solvable. It requires treating retrieval with the same engineering rigor you apply to other parts of your stack—not defaulting to whatever embedding model happens to be popular, but thinking carefully about chunking, hybrid query construction, and recall measurement. OpenSearch gives you the tools. Uchoa’s session gives you the blueprint for putting them together.
For ML engineers and architects building GenAI systems that need to be trustworthy in production—not just impressive in a demo—this is one of the most practically grounded sessions from OpenSearchCon Europe 2026.
