Wikipedia link race: choosing among thousands of options
The model chooses the next step from hundreds or thousands of links, demonstrating fast decisions with constrained outputs. The post includes a 4:04 video.
Jev + RAG: A Decision Layer Beyond Rerankers
The post discusses Jev (TypeSafe's model) combined with RAG, adding a decision layer that standard rerankers lack. It points out that embedding similarity and conventional rerankers optimize for topical closeness, not whether a passage actually contains usable evidence.
Jev + RAG adds a decision layer that standard rerankers lack. 🔍 @_avichawla’s breakdown shows the core issue: embedding similarity and conventional rerankers optimize for topical closeness, not whether a passage actually contains usable evidence. 📄 Jev (TypeSafe’s
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Semantic Querying Use Case with Jev and TypeSafe AI
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The author used Jev to run a live BS meter on every sentence of the Trump vs Kamala debate, with 5 yes/no questions per candidate, totaling 1,191 Jev calls, 1.18M tokens, 415 ms median latency, and $0.0497 total cost.
