This week, I focused on improving the engine’s move generation performance by applying additional optimization techniques such as quiescence search (Q-search) and transposition table lookups (TTLookup). These changes significantly reduced the effective branching factor and improved overall search efficiency, especially when combined with move ordering strategies. In parallel, I continued developing the agent orchestration pipeline by incorporating retrieval-augmented generation (RAG) and experimenting with prompt designs for more accurate and coherent explanation generation.
I also integrated the full pipeline with the frontend, enabling end-to-end interaction between the UI, engine, and explanation system. Additional integration tests were added to validate system behavior and ensure consistency across components. Moving forward, I will continue refining prompt engineering to improve explanation quality and further optimize the interaction between the search engine outputs and the LLM-generated reasoning.