Idea: Live Hallucination Detector

[mohamad-tohidi]

while the Rag system is generating the tokens, we buffer a small batch (20 tokens)
and we predict hallucinations online.

for this method, do we have to train the Hallucination Detector in an special way?
does your project support it?

[adaamko]

Hey, check this integration demo with langchain to support real time detection of hallucinations: https://github.com/KRLabsOrg/LettuceDetect/tree/main/lettucedetect/integrations/langchain Please note that the models were trained with the full context available, so the results might be worse.

[sanketbadgujar007-byte]

Nugen supports real-time hallucination detection without custom training. Instead of building and maintaining custom hallucination detectors for your RAG pipeline, you get production-grade monitoring out of the box.
Want to discuss integrating reliable detection into your RAG system? www.nugen.in/meet

[adaamko]

VLLM's semantic router also supports real-time hallucination detection: https://vllm-semantic-router.com/blog/halugate, they use LettuceDetect's architecture for the token level detection, it's completely open source if you want to check. I still think you cannot really get around custom training for your domain though.

[JohnnyTarrr]

For the API-based detection approach — VeroQ Shield does this in production. Each claim extracted from the LLM output is independently verified against external evidence (not self-consistency).

Returns per-claim verdicts with confidence scores, evidence chains, and corrections for contradicted claims. No local GPU needed.

from veroq import verify_output
result = verify_output(llm_response, context=source_docs)
# result.claims: [{text, verdict, confidence, evidence}]

Could plug in as a method="api" option alongside your transformer-based detector.

pip install veroq