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RAG for Code Generation - AI Hacker Cup Example

Offered By: Weights & Biases via YouTube

Tags

Retrieval Augmented Generation Courses Few-shot Learning Courses Code Generation Courses Competitive Programming Courses Similarity Search Courses Abstract Syntax Tree Courses AI Agents Courses

Course Description

Overview

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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.

Syllabus

RAG for Code Generation, an AI Hacker Cup example


Taught by

Weights & Biases

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