Grounding large language models using real-time content feeds and reference data

This invention describes a computer system that aims to make AI answers more truthful. It takes a question, asks an AI model, and then checks the AI's answer against reliable information. If the answer seems untruthful, it calculates a "hallucination score" and then specifically rewrites the original question based on that score to ask the AI again, aiming for a better, more accurate response.

Why it matters: While LLMs were prevalent in 2023, advancements in AI reasoning and prompt engineering between then and 2026 make iterative query rewriting based on a hallucination score more effective and practical for improving AI truthfulness.

Status: Under examinationA pending application. It has not been granted, and it has not been abandoned.
How hard to build: SpecializedSoftware logic, AI models, data integration

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