Show HN: 100% LLM accuracy–no fine-tuning, JSON only (github.com)

A recent project showcased a significant advancement in large language models (LLMs) with the introduction of the Triad Engine, which eliminates hallucinations during inference without requiring any fine-tuning. By utilizing a structured JSON domain guide that contains contextual information—including cultural norms, important events, and character details—the Triad Engine significantly enhances the accuracy of language models across various categories. For instance, tests conducted on historical scenarios, such as Ancient Rome in 110 CE, showed dramatic improvements in performance, with models like Claude 4.6 achieving a staggering 100% accuracy compared to their ungrounded counterparts. This development is momentous for the AI/ML community as it sets a new benchmark for reliable model inference, particularly in complex domains requiring intricate reasoning. The Triad Engine applies a model-agnostic approach, meaning it can be used with any existing LLM, enhancing models like GPT-5.2, Gemini 2.5 Pro, and various local open-source models. Importantly, this structured approach not only increases accuracy but also reduces token consumption, making it more efficient for real-world applications. These findings underscore the potential of cultural grounding to address persistent issues in LLMs, paving the way for more robust and contextually aware AI systems.