suman-kalavagunta/chess-coach-turningpoints

Chess Coach – Turning Point Explanations Dataset

Overview

This repository contains a curated, engine-grounded dataset for training language models to explain chess mistakes and turning points in a human coaching style.

The goal is explainability and pedagogy, not move calculation or engine strength.

What this dataset is (and is not)

✅ This dataset is for

• —Training LLMs to explain evaluation swings
• —Teaching coaching tone, structure, and pedagogy
• —Demonstrating engine-grounded LLM pipelines
• —Research in explainable AI for games

❌ This dataset is NOT for

• —Training a chess engine
• —Improving raw move strength
• —Replacing Stockfish or other engines
• —Competitive or tournament play

Important: The language model does not calculate chess. All chess truth comes from Stockfish and deterministic rules.

Dataset Structure

Each example corresponds to a single turning point extracted from a real game.

Format: JSONL (chat-style)

{
  "messages": [
    {
      "role": "user",
      "content": "<structured prompt with engine evals and features>"
    },
    {
      "role": "assistant",
      "content": "<coach-style explanation>"
    }
  ]
}

What the prompt contains

Each prompt typically includes:

• —Engine evaluation before the move
• —Engine evaluation after the move
• —Net centipawn swing
• —Best engine continuation
• —Deterministic features such as:
• —Development gap
• —King safety signals
• —Tactical motifs
• —Material imbalance
• —Output constraints (format, tone, tense)

What the response contains

Each response follows a coaching template, usually including:

1. 1.Net result summary
2. 2.What changed in the position
3. 3.What to do differently next time
4. 4.Reflective questions
5. 5.A short practice drill

Dataset Splits

The dataset follows standard Hugging Face conventions:

• —train – ~90% of examples
• —validation – ~10% of examples

Suitable for Supervised Fine-Tuning (SFT) using LoRA / QLoRA.

How the dataset was generated

High-level pipeline:

1. 1.Parse multi-game PGN files
2. 2.Identify candidate turning points
3. 3.Evaluate positions using Stockfish
4. 4.Extract deterministic chess features in Python
5. 5.Construct structured prompts
6. 6.Generate explanations using a teacher LLM
7. 7.Apply strict validation rules
8. 8.Emit clean (prompt → response) pairs

This design intentionally separates:

• —Truth → engines + rules
• —Explanation → language model

Intended Use

This dataset is well-suited for:

• —Fine-tuning instruction/chat LLMs
• —Building chess coaching or educational tools
• —Studying small, high-quality datasets for behavior shaping
• —Demonstrating hybrid symbolic + neural systems

Limitations

• —Explanations reflect engine evaluations, not human intuition
• —Focused on turning points, not full game narration
• —Style reflects the constraints used during generation
• —Coverage depends on the source PGN data

License

This dataset is released under the Apache 2.0 License.

Acknowledgements

• —Stockfish – for world-class chess evaluation
• —Hugging Face – for open tooling and hosting
• —The open chess community – for publicly available PGN data

Disclaimer

This dataset and any derived models are provided for educational and research purposes only.