LLMs confidently suggest chess moves that aren't on the board. How I verify every response with code

The author analyzes the issue of hallucinations in Large Language Models (LLMs) when playing chess. Despite their ability to generate text, these models often suggest impossible moves, ignoring the actual configuration of pieces on the board. The article emphasizes that simple prompt instructions fail to ensure reliability. Instead, the author proposes using programmatic validation for every move generated by the LLM. This approach allows for verifying the legality of actions according to chess rules, filtering out errors and AI hallucinations. The article is useful for developers integrating LLMs into systems that require strict logic and rule compliance, where the cost of errors is high. The author shares their experience in building tools for automated AI response control, ensuring the model's interaction with the game engine remains accurate.