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LLMs struggle with rule-based domains like chess, developer finds

Large language models like GPT-4 and Claude struggle with rule-based domains such as chess because they process game states as text strings rather than understanding the underlying rules. This leads to confident but incorrect assertions about game legality and positions. To address this, a developer created aichess.guru, an AI chess coach that separates the LLM's role to that of a communication layer, using deterministic libraries like chess.js for game logic and Stockfish for evaluations. The LLM's function is limited to explaining verified facts from these engines in plain English, preventing it from generating incorrect game states or moves. AI

IMPACT Highlights the need for deterministic systems to handle rule-based tasks, with LLMs acting solely as communication layers.

RANK_REASON Developer describes a practical application and limitation of LLMs in a specific domain.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with rule-based domains like chess, developer finds

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Chapri ·

    Why LLMs are terrible at chess (and what I did about it)

    <p>Ask GPT-4 or Claude to play a real game of chess and it will, with total confidence, try to move a knight like a bishop. Or capture its own queen. Or castle out of check. I've watched a frontier model announce "checkmate" on a board where the king had four legal escape squares…