Building an AI Chess Coach: Take Take Take

Anant Dole and Asbjorn Steinskog of Take Take Take presented their work on building an AI chess coach, detailing the technical pipeline and the challenges involved in creating a useful and accurate tool. The presentation, delivered at an AI Engineer Europe event, highlighted the limitations of current LLMs in strategic game playing and outlined their approach to overcoming these hurdles.

What is Take Take Take?

Take Take Take is an Android application designed to provide users with AI-powered chess coaching. After each game, the app presents a review of the game, highlighting key moments, tactical patterns, blunders, and positional mistakes. The core of this review is an AI pipeline that analyzes the game, extracts relevant context, and generates commentary. The goal is to provide feedback that is both instantaneous and accurate, enhancing the user's learning experience.

The Evolution of AI in Chess

The presentation traced the history of AI in chess, starting with Claude Shannon's 1949 paper on programming a computer to play chess. A significant milestone was Deep Blue's victory over Garry Kasparov in 1997, which demonstrated the power of brute-force search algorithms. More recently, AlphaZero, developed by DeepMind, showcased a new approach using reinforcement learning and neural networks to achieve superhuman performance in chess and other games. The emergence of Large Language Models (LLMs) like GPT has also shown promise, but as Dole and Steinskog pointed out, LLMs are not inherently suited for strategic games like chess due to their training on language rather than deep strategic reasoning.

LLMs Struggle with Chess Strategy

Dole and Steinskog emphasized that while LLMs excel at generating natural language and understanding context, they often struggle with the complex calculations and strategic planning required for high-level chess play. They noted that while LLMs can be trained on vast amounts of chess data and can sometimes produce good moves, their underlying architecture is not optimized for the kind of move prediction and evaluation that specialized chess engines perform. This limitation means that LLMs alone cannot reliably play chess at a competitive level, often making blunders or failing to grasp strategic nuances.

Building the AI Chess Coach Pipeline

The Take Take Take chess coach utilizes a multi-stage pipeline to deliver its analysis. The process begins with a chess engine, specifically Stockfish, a leading open-source chess engine, which analyzes the game and identifies the best moves and key strategic elements. This information is then fed into a context extraction module. This module employs tactical detectors and models like Maia (a chess engine trained on human games) to identify specific patterns, blunders, and positional mistakes. The extracted context is crucial for generating meaningful commentary. The system is designed to limit hallucinations, ensuring that the tactical advice provided is grounded in realistic chess principles.

The pipeline aims to balance latency and quality. For a seamless user experience, the coaching feedback needs to be almost instantaneous, ideally appearing within three seconds. However, achieving high-quality, in-depth analysis requires more reasoning effort, which can take longer. The team has experimented with different LLMs, including Gemini 3 Flash, Claude Opus 4.6, and GPT-5 Mini, to find the optimal balance. They found that while larger models offer better quality, they often come with higher latency, necessitating careful tuning to meet user expectations.

Closing the Loop with Autonomous Agents

To continuously improve the AI's performance and commentary, Take Take Take incorporates a feedback loop using autonomous agents. When a user downvotes a piece of commentary, the agent is triggered. It first performs triage, running a commentary-triage skill to classify the issue. Then, it investigates the problem by spawning a chess-expert subagent to verify its changes. If a fix is identified, the agent edits prompts or detectors. If stuck or uncertain, it collaborates by asking questions on Slack. This process allows the system to learn from user feedback and refine its analysis over time, ensuring the commentary remains accurate and relevant.

Learnings and Future Directions

The team shared several key learnings from their development process:

• Separate pipeline from language generation: Domain-specific systems for analysis, like chess engines, should be kept separate from LLMs used for natural language generation over structured data.
• Close the loop with autonomous agents: Utilizing agents to directly act upon user feedback in real-time is essential for continuous improvement.
• Build a clear context extraction model: Designing the context extraction as a data model contract, with every field precisely defined, is crucial for reliable analysis.
• Automated evals help with faster iteration: Using a suite of automated tests allows for quicker testing of new models as they are released.
• Use SMEs for evaluating quality output: While LLMs can assist in evaluation, subject matter experts (SMEs) should have the final say in assessing the quality of the output.

The presentation concluded with an invitation for attendees to participate in live chess simul games, with a QR code provided for sign-ups. Winners of the speed simul games will receive wooden chess boards. The event highlighted the practical application of AI in enhancing user experience and the ongoing challenges in building sophisticated AI systems for complex domains like chess.