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This is a Quantitative Study study.

October 23, 2025

Quantifying the RAG Advantage: A Multi-Metric Benchmark for LLM-based Code Generation

Key Points

Benchmarking developed for assessing performance metrics in large language models and code generation.
Enhanced prompts using Information Retrieval improved performance on algorithmic problems by utilizing relevant external knowledge.
Data-driven approaches allowed for a reliable evaluation framework to address limitations in LLMs when facing complex tasks.
The multi-metric system facilitates understanding of how RAG can enhance problem-solving in code generation applications.

Abstract

The recent advancement of Large Language Models (LLMs) has demonstrated remarkable capabilities in solving programming challenges. However, despite their proficiency, LLMs often suffer from hallucination and limited performance on unfamiliar or complex tasks. Retrieval-Augmented Generation (RAG) has emerged as a promising solution to address these limitations by supplementing prompts with relevant external information. In this paper, we propose a benchmark to assess the efficacy of RAG in solving algorithmic problems by integrating a curated database of 120 LeetCode problems, each paired with corresponding solutions and explanations. An Information Retrieval (IR) system was employed to construct enhanced prompts for solving novel problems.

Quantifying the RAG Advantage: A Multi-Metric Benchmark for LLM-based Code Generation

Key Points

Abstract

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