Bridging Chess Mastery and AI Innovation: The Making of LLM-ChessCoach

Introduction

The advent of AI technologies like GPT-3 has opened new frontiers in various fields, chess being no exception. As a tech enthusiast and chess aficionado, I found myself at the confluence of these two worlds, leading to the creation of LLM-ChessCoach. This tool isn’t just about analyzing chess games; it’s about deeply understanding them through the lens of advanced AI.

Technical Overview of LLM-ChessCoach

LLM-ChessCoach stands out due to its unique integration of GPT with chess analytics. The tool functions by downloading games from online chess platforms, and using Python scripts like — export_lichess_games.py for data extraction. The heart of the tool lies in — analyze_games.py, where the magic of GPT comes into play. It's here that each game is dissected, move by move, using a blend of natural language processing and chess expertise embedded within GPT.

For this Project, I have relied on GPT 3 (Primarily to save cost), but I will slowly start experimenting with more advanced GPTs as I add more functionality.

Development Journey and Challenges

The journey to create LLM-ChessCoach was both intellectually stimulating and technically challenging. One significant challenge was ensuring the accurate interpretation of chess games by GPT-3. This involved not just programming puzzles, but also an understanding of chess strategies and common pitfalls to make sure the LLM was not hallucinating.

We’re also batch-processing multiple games due to the slow-ish nature of responses from the LLM leading to a bad user experience if it was one at a time.

Overall, I would split this project into 3 pieces —

1. Capturing game data from a data store. In this case it is Lichess, but it can be any data store of chess games like Chess.com, manually generated PGNs, etc.
2. LLM integration and prompt engineering to provide input as games gather responses from the model and save the results. Given the good documentation of the OpenAI API, the hardest part here was the prompt engineering to be able get the response in a format I thought would be helpful to the user.
   My prompts are something like this for the overall analysis:
   Prompt:
   f” You are a chess grandmaster rated 3200. We are analyzing multiple chess games between {white_player} and other players:\n\n Provide your overall analysis of the player {user_alias} and also recommendations for how they can improve as a player. You don’t have to use specific move combinations unless it is to appreciate a checkmate combo or provide missed checkmate combos. The games could be across different time formats. Keep that into account in your analysis. Focus more on subjective feedback. Any specific material you can refer them to will also be helpful. :\n\n{pgn}”
3. StreamLit based UI Development- Since this was my first time playing around with a StreamLit based UI, it took me some time to understand the various components of the development process. The most annoying piece was that the whole app refreshes every time any button is clicked, thus refreshing some variables if not defined correctly. This was annoying especially because I needed page transitions on my app. I’ve realized StreamLit is probably best used for more static-based applications without too much Dynamic change. In the future, I might move to a framework like Flask along with JavaScript to be able to deal with more dynamic use cases.

User Experience and Screenshots

Starting Page of the Web App

Add the User Name and from what date you want games to be analyzed.

If it’s an existing run then you provide the Run ID.

The results page will contain a detailed analysis of each game as well as an overall analysis of players across all games.

Practical Applications and Future Enhancements

LLM-ChessCoach goes beyond mere game analysis; it offers strategic insights, identifies patterns, and even suggests areas of improvement tailored to each player’s style.

Future enhancements could include real-time analysis, personalized coaching modules, and integration with additional chess platforms.

I am exploring the Lichess API to see if along with the move numbers the LLM provides for a specific game’s analysis, I can set up a board at the position within the UI to allow the user to play around right there. That along with integration with the Lichess engine should help evaluate different positions.

Engagement and Conclusion

The fusion of AI and chess in LLM-ChessCoach is just the beginning of a new era in sports analytics and training. As developers and enthusiasts, we stand at a pivotal moment where our creativity can redefine the boundaries of technology in enhancing human skills. I invite you to share your thoughts on how AI can further revolutionize chess training and other areas of our intellectual pursuits.

Added Details —

Github repo: https://github.com/akmenon1996/LLM-ChessCoach/tree/master

Lichess Account: https://lichess.org/@/ZeusStrikes420

Acknowledgments —

1. OpenAI and ChatGPT — Quite a bit of the work put into this project was through the assistance of existing LLM tools, including the first picture of this article that was created using Dall-e.
2. Lichess API — Lichess is by far my favourite site to play chess on and their API integration being as good as it is has made life quite simple on this project.

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