RAG Hallucination Detection and Multi-Project Tracing
RAG systems still hallucinate. Your LLM confidently states facts not in your documents. LongTracer catches this before it reaches your users - verifying every claim against your source documents using a two-stage STS and NLI pipeline. No LLM dependency. No vector store required. Just strings in, trust score out.
pip install longtracerClaim-level verification with full trace. Works with any RAG framework.
Verifies every individual claim in the LLM response against source documents. Pinpoints exactly which statements are hallucinated.
Returns a trust score representing the proportion of supported claims. Threshold-based filtering for production quality gates.
STS relevance scoring runs alongside LLM generation. Minimal latency impact on your RAG system.
Works with any RAG framework. Just strings in, verification out. No API keys, no LLM costs for verification.
Track verification results across multiple projects and time periods with pluggable storage backends.
Native integration helpers for LangChain and LlamaIndex. Drop into your existing pipeline in minutes.
We build production RAG systems with LongTracer integrated for quality monitoring. Schedule a free consultation.