Show HN: I Built an LLM Engine, That Test LLM on Boolean Logic (github.com)

A developer has introduced a groundbreaking LLM engine designed to accurately validate boolean logic in AI decision-making systems. This engine addresses a critical flaw in current large language models (LLMs), which often fail to detect contradictions in complex rules, leading to erroneous outcomes in applications such as loan approvals and compliance checks. The engine can evaluate potential conflicts in boolean rules in under 10 milliseconds, providing a much-needed deterministic layer that LLMs lack. Benchmark tests reveal that even the most advanced models (e.g., a 70 billion parameter model) incorrectly answer around 20% of boolean logic questions, highlighting the necessity for such validation tools. The engine enables exhaustive enumeration of rules without external dependencies, examining all possible input combinations for contradictions, tautologies, and equivalences. It seamlessly integrates with LLMs, allowing them to focus on mapping natural language to mathematical expressions while the engine handles the logical analysis. This innovative approach not only significantly enhances the reliability of AI systems but also sets a new standard for the rigorous verification of decision-making processes in AI—showing that reliance on LLMs alone is inadequate for tasks requiring precise logical reasoning.