This episode introduces PairCoder, a framework that enhances code generation using large language models (LLMs) by mimicking pair programming. PairCoder features two AI agents: the Navigator, responsible for planning and generating multiple solution strategies, and the Driver, which focuses on writing and testing code based on the Navigator's guidance.The episode explains how PairCoder iteratively refines code until it passes all tests, leading to significant improvements in accuracy across benchmarks. Evaluations show that PairCoder outperforms traditional LLM approaches, with accuracy gains of up to 162%. Despite slightly higher API costs, its accuracy makes it a cost-effective solution. Future directions include incorporating human feedback and advanced test case generation. PairCoder's collaborative AI approach offers a new path for more intelligent and efficient code generation.https://arxiv.org/pdf/2409.05001

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