The Factual Attribution Lock (Zero Hallucination)
Financial document analysis (e.g., answering questions on SEC Form 10-K) has zero tolerance for hallucinated numbers. Relying purely on semantic vector search often retrieves adjacent, incorrect numbers from surrounding sentences.
How would you architect a post-generation validation layer to guarantee that the LLM's generated answers contain zero hallucinations and are strictly attributed to the source documents?