# Jevjitsu: Or, How We Tried Generalized Classifiers on Everything

DevFeed: [Jevjitsu: Or, How We Tried Generalized Classifiers on Everything](<https://devfeed.tech/articles/jevjitsu-or-how-we-tried-generalized-classifiers-on-everything-67011.md>)

Original publisher: [Read original article](<https://qdrant.tech/blog/jevjitsu/>)

Author: info@qdrant.tech (Andrey Vasnetsov)

Published: 2026-10-08T00:00:00Z

Content type: article

Language: en

Sources: [Qdrant Blog on Qdrant - Vector Search Engine](<https://devfeed.tech/sources/qdrant-blog-on-qdrant-vector-search-engine.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM observability](<https://devfeed.tech/topics/llm-observability.md>), [tgi](<https://devfeed.tech/topics/tgi.md>)

Tags: [approximate-nearest-neighbor-search](<https://devfeed.tech/tags/approximate-nearest-neighbor-search.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bert](<https://devfeed.tech/tags/bert.md>), [classification](<https://devfeed.tech/tags/classification.md>), [classifiers](<https://devfeed.tech/tags/classifiers.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fasttext](<https://devfeed.tech/tags/fasttext.md>), [hnsw](<https://devfeed.tech/tags/hnsw.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [image-search](<https://devfeed.tech/tags/image-search.md>), [knn-algorithm](<https://devfeed.tech/tags/knn-algorithm.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [matching](<https://devfeed.tech/tags/matching.md>), [ml](<https://devfeed.tech/tags/ml.md>), [model](<https://devfeed.tech/tags/model.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [openrouter](<https://devfeed.tech/tags/openrouter.md>), [prompting](<https://devfeed.tech/tags/prompting.md>), [qdrant](<https://devfeed.tech/tags/qdrant.md>), [recommender-system](<https://devfeed.tech/tags/recommender-system.md>), [saas](<https://devfeed.tech/tags/saas.md>), [simaes-networks](<https://devfeed.tech/tags/simaes-networks.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [training](<https://devfeed.tech/tags/training.md>), [transformer](<https://devfeed.tech/tags/transformer.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [type-safety](<https://devfeed.tech/tags/type-safety.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>), [vector-search-engine](<https://devfeed.tech/tags/vector-search-engine.md>), [vectors](<https://devfeed.tech/tags/vectors.md>), [word2vec](<https://devfeed.tech/tags/word2vec.md>)

## AI overview

The article reports experiments using Jev, a decision model for classification, in search reranking, product result diversity, query understanding, and semantic chunking. It describes improved ranking and chunking metrics in the tested datasets, while noting trade-offs such as latency, API cost, and the risks of filtering ambiguous queries.

## Source excerpt

Classification is one of the oldest problems in machine learning. Unlike modern decoding transformers, most classification models were non-autoregressive by nature. Before Jev, the common approaches were training your own classifier, which needs labels, using a zero-shot classifier (compared in this Hugging Face benchmark), or prompting an LLM with some hacky type safety on top. There were also meta-ML methods, but none of them reached the mainstream. With the introduction of Jev, a decision model from TypeSafe served through OpenRouter, the taxonomy of classifiers has changed yet again: