Loop Based Workflow
Loop Based Workflow is an AI coding technique that structures problem-solving through iterative cycles rather than attempting single-pass solutions. The approach treats coding tasks as repeatable processes where an AI system generates output, evaluates results, and refines its work across multiple iterations. Each loop cycle typically involves executing code, assessing correctness or quality, and making adjustments based on feedback or error detection.
Core Mechanism
The workflow operates by establishing a feedback loop between code generation and evaluation phases. Rather than relying on the AI to produce perfect code immediately, the technique acknowledges that iterative refinement often produces more robust solutions. The AI can test its output, identify issues, and apply corrections systematically across subsequent iterations until reaching a satisfactory result.
Practical Application
This technique gained visibility through demonstrations by Gary Explains, which showed how structured iteration improves AI coding performance. The approach is particularly useful for complex problems where requirements may not be fully clear upfront or where edge cases emerge during development. By cycling through generation and testing phases, developers can catch and resolve issues incrementally rather than requiring manual intervention at the end.