#hallucination

Audit long-form LLM output for factual accuracy, hallucination risk, source quality, and actionable edit suggestions.

AI Quality Engineering framework for evaluating LLM and RAG applications using automated prompt testing, hallucination detection, and AI evaluation metrics.

Stop Claude from lying to you. A Claude Code skill that fact-checks every claim against real sources — code files, live docs, web search — and flags anything it can't confirm.

Deterministic fact-checker for AI coding agents — verify an agent's claims about its own work against the real code, git diff & logs. Local MCP server, cited verdicts, refuses to bluff.

An AI leaderboard scoring chatbots on how much they bullshit vs. give honest answers to unanswerable questions. Works with paid APIs and free/local models.

Multilingual, RAGAS-compatible RAG evaluation with language-native judge prompts and Claude as the judge.

A thin, CI-gated evaluation gate for RAG & LLM systems — golden set, band-based pass/warn/fail gates, LLM-judge or heuristic scorers. pip install raggate

Conservative grounding gate for RAG and fine-tuned LLMs — pass, rewrite, or abstain.

Label every claim in AI output as (u) given, (m) checked or (g) generated, in plain text, so a guess can't quietly become a "fact" when text passes between people and AI agents. Includes a swarm test (the spoke and wheel test), a parser and a gate. Early findings.