Hi everyone, I’m a Google Cloud Innovator from Indonesia. I’ve been conducting stress-tests on Gemini 3 Flash via Vertex AI Studio and discovered a fascinating edge-case I call ‘The Frankenstein Hallucination’.
My experiment shows that while Grounding with Google Search is powerful, it can sometimes lead to a ‘more confident liar’—where the model stitches together disparate valid facts to support a false premise.
I am proposing a ‘Sanad’ (Data Provenance) framework for Enterprise RAG to mitigate this. Would love to hear your thoughts on how we can improve grounding confidence thresholds to prevent this ‘stitching’ effect!
Hi @Farisds Very interesting finding this is a known RAG issue where the model combines correct facts into a wrong conclusion Grounding improves accuracy but does not guarantee logical correctness. To reduce this you can add a premise validation step before answering require every claim to be linked to a specific source and add a secondary verification model to check consistency. Your Sanad provenance idea makes sense especially if it tracks evidence at the claim level not just the document level. This is an important topic for enterprise AI thanks for sharing your research
Hi Aleksei @a_aleinikov , thank you for the insightful feedback! I completely agree that grounding alone doesn’t solve the logic gap. Adding a premise validation step is a great suggestion to enforce consistency.
Regarding the Sanad provenance idea, my goal is indeed to implement a ‘Chain of Evidence’ at the claim level to prevent the model from stitching unrelated facts together. I’m currently exploring a secondary verification model to act as a ‘logical auditor’ before the final response is generated.
Great to hear this is relevant for enterprise AI. Let’s keep pushing the boundaries of RAG reliability!