Europe 2026
Building a Chess Coach — Anant Dole and Asbjorn Steinskog, Take Take Take
Anant Dole , Asbjorn Steinskog , Take Take Take
Overview
This talk details the development of an AI-powered chess coach, focusing on how to bridge the gap between traditional chess engines and Large Language Models (LLMs). The core challenge addressed is LLMs' inherent weakness in calculation and reasoning for games like chess, despite their ability to explain concepts. The solution involves a pipeline that leverages strong chess engines for analysis and LLMs for generating human-readable explanations, with an autonomous agent loop for continuous improvement.
Who should watch
- AI Engineers building applications that require deep reasoning or game-playing capabilities.
- Product Managers and builders looking to integrate LLMs into complex, real-time user-facing products.
- Developers interested in improving LLM output through structured data pipelines and agentic feedback loops.
- Anyone curious about the practical application of LLMs in domains traditionally dominated by specialized algorithms.
Key takeaways
- LLMs, while capable of language generation, struggle with the precise calculation and strategic depth required for games like chess, often leading to hallucinations.
- Traditional chess engines like Stockfish excel at calculating optimal moves but cannot provide nuanced explanations.
- A hybrid approach, using Stockfish for move evaluation and context extraction, then feeding this structured data to an LLM for explanation, can overcome LLM limitations.
- An autonomous agent system, using tools like Cloud Code, can ingest user feedback on generated commentary, triage issues, modify prompts or detectors, and iterate on explanations, closing the loop for continuous improvement.
- For consumer-facing applications, balancing latency and quality is crucial; models like Gemini Flash can provide near-instantaneous feedback, while more powerful models might be reserved for less time-sensitive features.
- Automated evaluation (evals) using domain expertise and structured scenarios is vital for benchmarking and improving LLM performance in specific tasks.
- Separating the data pipeline from the language generation process and building clear context extraction models are key to achieving desired performance and quality.
Notable quotes
*LLMs often hallucinate because they're trained on language; they can't calculate.*
*We want everything to be grounded in the information that we give it.*
*It's really important to separate that sort of data pipeline from the language generation.*
Unofficial community note. Prefer the recording for nuance.