INPUT TEXT
Answer and source documents enter the verification pipeline.
Hallucination Hunter
Claim-level hallucination detection platform that decomposes model responses, grounds individual claims with NLI and semantic similarity, and surfaces explainable verification heatmaps.
HALLUCINATION HUNTER
LLM EVALUATION
Claim-level hallucination detection platform that decomposes model responses, grounds individual claims with NLI and semantic similarity, and surfaces explainable verification heatmaps.
SYSTEM / MEDIA LOOP
THE PIPELINE
Hallucination Hunter decomposes answers into claims, grounds them against evidence, and verifies each claim with embeddings and NLI.
SYSTEM NOTES
HOW IT WORKS
The pipeline keeps research, creation, publishing, and learning inspectable as one operating loop.
CLAIM EXTRACTION
The response is split into atomic claims for claim-level checking.
EMBEDDING LAYER
Semantic search pairs claims with relevant evidence from the source.
NLI VERIFICATION
DeBERTa-v3 evaluates entailment, contradiction, and uncertainty.
VERIFICATION HEATMAP
Evidence is aggregated into an explainable verification result.
EXPLANATION
Users can inspect which claims are supported, contradicted, or unsupported.
TECHNICAL CONTEXT
THE SYSTEM, BRIEFLY
Claim-level hallucination detection platform that decomposes model responses, grounds individual claims with NLI and semantic similarity, and surfaces explainable verification heatmaps.
The pipeline decomposes an AI answer into individual claims, grounds each claim against source text using NLI and semantic similarity, then presents readable evidence so users can see which parts are supported, contradicted, or unsupported — without relying on a single opaque score.
AI reliability needs claim-level evidence, not a single opaque confidence score.
PythonFastAPIPyTorchDeBERTa-v3Hugging Facescikit-learnDocker