AlphaCodium is a new approach to code generation using large language models (LLMs), designed to improve performance on code problems. Unlike traditional natural language generation techniques, code generation requires precise syntax matching, handling of edge cases, and attention to detail. AlphaCodium is a test-based, multistage, code-oriented iterative flow that significantly improves LLM performance on code tasks. It was tested on the CodeContests dataset, which includes competitive programming problems from platforms like Codeforces. The flow consistently and significantly improves results, with GPT-4 accuracy (pass@5) increasing from 19% to 44% when using AlphaCodium. The flow consists of two main phases: a pre-processing phase where the problem is reasoned about in natural language, and an iterative code generation phase where code is generated, run, and fixed against public and AI-generated tests. Key elements of the flow include generating additional data such as problem reflection and test reasoning, and enriching public tests with additional AI-generated tests. The flow also incorporates novel code-oriented design concepts, such as YAML structured output, bullet point analysis for semantic reasoning, generating modular code, soft decisions with double validation, encouraging exploration, and test anchors. AlphaCodium outperforms previous works, including AlphaCode, while requiring significantly fewer computational resources. It is applicable to a wide range of code generation tasks and is more effective than simpler benchmarks like HumanEval. The flow is designed to be efficient and effective, with structured output enabling multiple stages to be combined into a single LLM call. The results show that AlphaCodium significantly improves the performance of LLMs on code generation tasks, making it a valuable tool for code generation.AlphaCodium is a new approach to code generation using large language models (LLMs), designed to improve performance on code problems. Unlike traditional natural language generation techniques, code generation requires precise syntax matching, handling of edge cases, and attention to detail. AlphaCodium is a test-based, multistage, code-oriented iterative flow that significantly improves LLM performance on code tasks. It was tested on the CodeContests dataset, which includes competitive programming problems from platforms like Codeforces. The flow consistently and significantly improves results, with GPT-4 accuracy (pass@5) increasing from 19% to 44% when using AlphaCodium. The flow consists of two main phases: a pre-processing phase where the problem is reasoned about in natural language, and an iterative code generation phase where code is generated, run, and fixed against public and AI-generated tests. Key elements of the flow include generating additional data such as problem reflection and test reasoning, and enriching public tests with additional AI-generated tests. The flow also incorporates novel code-oriented design concepts, such as YAML structured output, bullet point analysis for semantic reasoning, generating modular code, soft decisions with double validation, encouraging exploration, and test anchors. AlphaCodium outperforms previous works, including AlphaCode, while requiring significantly fewer computational resources. It is applicable to a wide range of code generation tasks and is more effective than simpler benchmarks like HumanEval. The flow is designed to be efficient and effective, with structured output enabling multiple stages to be combined into a single LLM call. The results show that AlphaCodium significantly improves the performance of LLMs on code generation tasks, making it a valuable tool for code generation.