RAG for Code Generation - AI Hacker Cup Example

Dive into the world of LLM-powered competitive programming through an in-depth analysis of Retrieval Augmented Generation (RAG) for code generation agents. Presented at the NeurIPS HackerCup AI Competition (HAC) 2024 lecture series, this 52-minute video explores how RAG enhances agent-based strategies for complex coding challenges. Examine the specific challenges of competitive programming with LLMs and learn about designing RAG architectures for robust code generation. Discover the implementation of AST-based similarity search for rapid code retrieval and the integration of structural and semantic similarity in a multi-stage retrieval process. Explore techniques for enhancing few-shot learning with enriched example programming scenarios and promoting AI self-reflection and iterative improvement. Gain insights into how advanced agentic systems leverage existing problem solutions, employ multi-agent strategies, and apply cutting-edge techniques to push the boundaries of AI agents in competitive programming.