I Thought Everyone Understood Loop Engineering. I was Wrong!

Loop engineering, a term gaining traction in AI development, refers to the iterative process of refining prompts and outputs when working with large language models, according to a Medium post by an unnamed author. The post clarifies that loop engineering is not a complex academic concept but a practical method already used by developers daily, exemplified by the cycle of prompting an LLM, reviewing its output, and adjusting the input. The author emphasizes the need to understand fundamentals like loop engineering amid the rapid evolution of agentic coding.

Member-only story I Thought Everyone Understood Loop Engineering. I was Wrong It’s strange how fast we are moving — prompt engineering → context engineering → and now loop engineering graph engineering So, is this just wordplay, or how does it make sense? That question came from Jim, who is currently learning software engineering in the AI era. And honestly, I sometimes get it wrong when I throw around terms like these without stopping to explain the basics. Agentic coding is developing too fast. Sometimes we have to slow down and understand the fundamentals before chasing the next thing. Loop engineering is one of those terms getting tossed around everywhere right now. Most people assume it is something complicated and academic. It is not. So — what is loop engineering? In this post, I will make it very clear with a practical example you can follow along. You Are Already Running a Loop Before we get to loop engineering, we need to talk about what you are already doing every single day when you work with an LLM. You open Claude or ChatGPT and type a prompt The model gives you back an output You read it, decide it is not …