RAG for Code Generation, an AI Hacker Cup example

Explore the advancements in LLM-powered competitive programming through this in-depth analysis of Retrieval Augmented Generation (RAG) for code generation agents. As presented at the NeurIPS HackerCup AI Competition (HAC) 2024 lecture series, this video showcases how RAG can be utilized to enhance agent-based strategies for tackling complex coding challenges.
Key discussion points include:
- Addressing the specific challenges of competitive programming with LLMs
- Designing RAG architectures for robust code generation
- Implementing AST-based similarity search for rapid code retrieval
- Integrating structural and semantic similarity in a multi-stage retrieval process
- Enhancing few-shot learning with enriched example programming scenarios
- Promoting AI self-reflection and iterative improvement
Discover how advanced agentic systems can leverage existing problem solutions, employ multi-agent strategies, and apply state-of-the-art techniques to push the boundaries of AI agents in the competitive programming arena.

[@harshdeepsingh3872]

can you make video on free compute resources available for free , for open source model