The paper introduces AlphaCodium, a novel approach to code generation using large language models (LLMs). Unlike common natural language problems, code generation requires precise syntax, handling edge cases, and addressing numerous small details. AlphaCodium is a test-based, multi-stage, iterative flow designed to improve LLMs' performance on code problems. The method was tested on the CodeContests dataset, which includes competitive programming problems from platforms like Codeforces. The results show that AlphaCodium consistently and significantly improves the accuracy of LLMs, with GPT-4 achieving a pass@5 accuracy of 44% on the validation set, up from 19% with a single well-designed prompt. Key features of AlphaCodium include generating additional data, such as problem reflection and test reasoning, and enriching public tests with AI-generated tests. The flow is divided into two main phases: a pre-processing phase for natural language reasoning and an iterative code generation phase for running and fixing code solutions against tests. The paper also discusses design concepts like YAML structured output, bullet point analysis, modular code generation, soft decisions with double validation, and test anchors. AlphaCodium outperforms previous methods like AlphaCode and CodeChain while using significantly fewer LLM calls, demonstrating its efficiency and effectiveness in code generation tasks.The paper introduces AlphaCodium, a novel approach to code generation using large language models (LLMs). Unlike common natural language problems, code generation requires precise syntax, handling edge cases, and addressing numerous small details. AlphaCodium is a test-based, multi-stage, iterative flow designed to improve LLMs' performance on code problems. The method was tested on the CodeContests dataset, which includes competitive programming problems from platforms like Codeforces. The results show that AlphaCodium consistently and significantly improves the accuracy of LLMs, with GPT-4 achieving a pass@5 accuracy of 44% on the validation set, up from 19% with a single well-designed prompt. Key features of AlphaCodium include generating additional data, such as problem reflection and test reasoning, and enriching public tests with AI-generated tests. The flow is divided into two main phases: a pre-processing phase for natural language reasoning and an iterative code generation phase for running and fixing code solutions against tests. The paper also discusses design concepts like YAML structured output, bullet point analysis, modular code generation, soft decisions with double validation, and test anchors. AlphaCodium outperforms previous methods like AlphaCode and CodeChain while using significantly fewer LLM calls, demonstrating its efficiency and effectiveness in code generation tasks.