Jevjitsu: Or, How We Tried Generalized Classifiers on Everything

Jev, a TypeSafe decision model served through OpenRouter, is presented as a generalized classifier that accepts runtime labels, produces constrained outputs, and aims to provide a faster, less costly alternative to generative LLM prompting for classification tasks. Experiments across search and retrieval found that Jev improved reranking performance on NFCorpus, with its single-request scoring method offering nearly the benefit of a ten-request iterative approach and outperforming tested local cross-encoders. In e-commerce search, Jev reranking increased relevance but could make results more repetitive, while combining its relevance scores with Maximal Marginal Relevance reduced near-duplicates and retained relevance above the hybrid-search baseline. For query understanding, Jev-based product-category routing improved Amazon-C4 results when high-confidence predictions filtered candidates and moderate-confidence predictions boosted them, although ambiguous short queries such as “apple” showed that confident classification can still harm results. Jev was also used for semantic document chunking by identifying topic changes between sentences, producing improvements over fixed, recursive, and embedding-based splitters on QASPER evidence retrieval, though at API cost. The authors recommend semantic chunking as an accessible RAG application, Jev-plus-MMR for product-search diversity, and cautious testing of taxonomy filters, concluding that generalized decision models may be valuable where their quality, latency, and cost trade-offs are acceptable.

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