Large Language Models (LLMs) have revolutionized software development and promised great efficiency gains, but their probabilistic nature still makes them unpredictable and unreliable: They frequently generate code that contains errors, defects, or fails to meet project standards. This paper introduces the Iterative Refinement Loop, a design pattern that addresses this reliability gap. The pattern establishes a feedback loop where validation tools–such as compilers, linters, and test suites–provide corrective feedback to the LLM. This guides the model through successive iterations to produce a correct, high-quality artifact, offering a robust, automatable mechanism that transforms an unreliable generator into a dependable partner for co-creating software.