2026-06-08 –, Palais Atelier
How does searching for new information often look? Loops: query, review results for relevance, rewrite the query, repeat… Until success, or until the user churns / the token budget burns.
This talk introduces a new instrument for search pipeline builders: propagating query-results relevance right inside the search algorithm of a search engine.
The relevance of search results is a use-case-dependent, capricious metric. Without access to the full dataset and visibility into the search algorithm, getting relevant results means either guessing the right query formulation or search engineers squeezing out the reranking (or context) budget to compensate for the search algorithm's required simplicity at scale.
What if your retriever could be guided by relevance feedback signals from a smart model (like a reranker or even a search agent) during the search process itself, achieving higher recall and discoverability of relevant results at a reasonable cost?
In this talk, I'll present our API for distilling relevance feedback from smart models directly into the vector search index.
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This talk is sponsored by Qdrant
Developer Advocate at Qdrant with 8 years of IT experience across software engineering, machine learning, and developer advocacy.
Holds a Technical University of Munich master's degree in Data Analytics and Engineering.
Passionate about NLP and Information Retrieval.
Believes in conference-, complaints- and memes-driven development:)