I built a chess coach that explains moves like a grandmaster instead of showing engine lines — powered by LLM

Reddit r/artificial Products
chess ai-coach llm chess-analysis stockfish pattern-detection open-source

Summary

A chess coaching tool that uses an LLM to explain moves in natural language like a grandmaster, replacing raw engine evaluations with contextual coaching narratives. It analyzes user games from Chess.com and Lichess with local Stockfish, detects recurring mistakes, and offers personalized chat and spaced repetition for blunders.

The problem I wanted to solve: Stockfish tells you *what* the best move is, but never *why*. Players under 1800 don't lose because they can't read centipawns — they lose because they don't understand plans, structures, key squares. **What the tool does:** 1. Imports your games from Chess.com or Lichess 2. Stockfish 17.1 WASM runs in your browser (fully local, nothing uploaded) 3. A pattern detector finds 18 types of recurring mistakes across all your games (missed forks, exposed king, bad bishop, neglected development...) 4. An LLM generates coaching narratives in the style of a 2700+ coach **Instead of:** -89 cp · Best: Nc3 Nf6 Be3 **The AI coach says:** > "Bd3 is premature — the bishop attacks nothing and blocks d3 where the queen may want to go. Nc3 was the right move: it defends d4, prevents Black's ...e5 counterplay, and leaves the bishop free to settle on Be3 or Be2 depending on Black's plan." You can also **chat with the coach** — it knows your full game history, opening stats, specific weaknesses. Ask "why do I keep losing with Black in the French?" and it answers with data from YOUR games. Other features: spaced repetition (SM-2) on your own blunders, puzzle rush with real mistakes, 6-month progress tracking. Free tier: unlimited Stockfish. Pro ($14.99/mo, 15-day free trial): LLM coach + chat. https://chessmentorai.com Happy to discuss the prompting approach — getting the LLM to explain chess like a coach (not an engine) was the hardest part.
Original Article

Similar Articles

Language Models that Play Chess and Explain Their Moves

Hugging Face Daily Papers

The paper introduces Queen, a 4B-parameter chess-language model that explains its moves while playing at Grandmaster level, using a silent-expert chess encoder fused with an instruction-tuned LM via cross-attention and an iterative Bellman-style distillation loop that gains over 900 Elo points.

mlabonne/LFM2.5-230M-Chess · Hugging Face

Reddit r/LocalLLaMA

A 230M-parameter chess engine fine-tuned from LFM2.5-230M-Base using Stockfish distillation, capable of predicting chess moves with a language model interface and playing at around 2004 Elo.

Princeton researchers train a 4B LLM to reach 2700 Elo in chess (with no signs of a plateau when they stopped training) and can explain its moves accurately. They say the training technique can also be applied to other games, robotics, and computer use

Reddit r/artificial

Princeton researchers train a 4B-parameter LLM to reach 2700 Elo chess strength using a natural-language analogue of the AlphaZero algorithm, with architectural and algorithmic innovations that also allow it to accurately explain its moves. The authors say the technique generalizes to other games, robotics, and computer use.