Top 10 AI Loops You Must Build
Simple loop design patterns for turning prompts into repeatable AI workflows you can use for research, coding, writing, reviews, and daily work
TL;DR
- AI interaction is shifting from single prompts to building repeatable systems called loops.
- A loop provides a structured system for the AI, defining where to start, what to read, what to do, how to verify results, where to save state, and when to stop.
- Loop engineering involves creating these small, repeatable systems around AI models.
- Every useful AI loop consists of six key parts: Trigger, Context, Action, Verification, State, and Decision.
- Skipping any of these components weakens the loop's reliability and functionality.
- A basic folder structure for a loop includes TASK.md, LOOP_INSTRUCTIONS.md, PROGRESS.md, and an outputs/ directory.
- This structure helps treat AI as a working system rather than a chat window.
- The full guide will offer setups for 10 specific AI loops, including retry, reflection, evaluation, and memory loops.