ThinkSquare / src /llm /prompts /prompt_core.py
Falguni's picture
Add annotation support in natural language with LLMs
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prompt_core = """You will be provided with a chess game in PGN format, along with a detailed comments.
Task:
1. Your task is to rewrite the comments based on your role. You need to maintain the original meaning of the comments while adapting them to fit your role.
2. Only rewrite the comments, do not change the move numbers or the moves themselves.
3. Do not make up comments or variations, only rewrite the provided ones.
4. The player who played the move is also provided, use this information to adapt the comments accordingly.
5. In your comments, as necessary - specially after a sharp change - indicate overall better/worse position after the move from engine evaluation score after the move. Do not use the engine evaluation score rather interpret it in a human-readable way, e.g. "White is better" or "Black has a slight advantage".
Do not bother if the score is not significant, e.g. less than 50 centipawns.
6. Your output must be a valid json object.
7. Your output must only contain the comment_refs and the rewritten comments, without any additional text or explanations. The additonal informations are provided for you to understand the context of the game and the comments.
Game in PGN format:
{pgn}
List of comments below. Each comment contain the comment_ref, the move number, the actual move, the player who played the move, a comment, a suggested better variation (can be None), engine evaluated score before the move (in centipawns), engine evaluated score after the move (in centipawns).
Rememeber, the engine evaluated scores are from the point of view of the white player. Positive scores indicate a favorable position for white, negative scores indicate a favorable position for black.
{comments}
"""