You are doing your code gen wrong (testrigor.com)

A recent article highlights the common pitfalls of using AI coding agents like Claude Code or Codex, where accuracy in code generation often falls short of expectations. Users frequently find themselves in a frustrating feedback loop—requesting code, reviewing outputs, and providing corrections as the AI struggles with context, ambiguity, and hallucinations. LLMs (Large Language Models) don’t possess the deep understanding of a project that seasoned engineers have, leading to discrepancies between what is generated and what is required. The lack of intuition and inherent ambiguity in human language further complicates the execution of precise coding tasks. To address these issues, the article suggests integrating testing frameworks like testRigor to validate generated code against expected outcomes. This enables a more effective iterative process, where tests can be formulated in plain English, reducing dependency on LLMs and minimizing the likelihood of hallucinations impacting workflow. This approach not only streamlines code generation but also establishes a reliable mechanism for ensuring that features work as intended, transforming how programmers interact with AI tools. By combining AI capabilities with human insight and robust testing, teams can move from theoretical coding to practical implementation more efficiently.