LLM output verification with decision models

Python harness stress-testing Jev's confidence, its accuracy as a RAGTruth hallucination judge, and its use as a RAG reranker.

Local decision engine on zero-shot ModernBERT and SigLIP2 answering Choice, Noul, Score and Relation over text, documents and images.

Python toolkit that evaluates LLM, RAG and agent outputs and guards tool calls with typed decision-model questions.

Pre-registered benchmark of Jev as a medical hallucination check on MedHallu, versus fast LLMs, on accuracy, latency and cost.

Pre-registered benchmark where Jev makes every search and evidence decision for a 2B local model answering SimpleQA questions.

Paper and benchmark testing Jev as a zero-shot detector of AI alignment failures across 44 benchmarks and ten failure types.

Official TypeSafe cookbook recipes for guardrails, re-ranking, RAG passage classification, citation checks and more with Jev.

Official TypeSafe cookbook example for checking whether source context actually supports a claim.

OpenRouter cookbook: Jev checks a cheap model's draft against grounding constraints before accepting it or escalating to a stronger model.

Chinese X post measuring Jev in a RAG pipeline over TypeSafe's docs: correct top-ranked answers rose from 7 to 17 of 20.