Critique-and-Revise Loops — ChatGPT

ChatGPTAutomation From a course

A loop is simply feeding a model's own output back to it (or to a second prompt) for review and improvement, repeated until a quality bar is met.

The prompt

Step 1 — Draft: Write a 150-word product description for [product].
Step 2 — Critique: Now critique your own draft as a skeptical copy chief — list 3 specific weaknesses.
Step 3 — Revise: Rewrite the description fixing all 3 weaknesses.

What to replace

Swap these placeholders for your own details before running the prompt:

[product]your own value

Pro tip: Cap every loop at 2-3 iterations. Past that, quality gains flatten and the model can start "over-editing" good answers into worse ones.

How to use this prompt

Copy the prompt using the button above.
Replace [product] with your own details — the more specific you are, the better the output.
Paste it into ChatGPT and run it.
If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.

Learn the technique

Critique-and-Revise Loops

Module 1 — What Is a Prompt Loop · Loop & Agentic Engineering

Related prompts

Claude

Critique-and-Revise Loops — Claude

A loop is simply feeding a model's own output back to it (or to a second prompt) for review and improvement, repeated until a quality bar is met.

ChatGPT / Agent tools

The ReAct Pattern (Reason + Act)

ReAct loops interleave reasoning ("what should I do next?") with actions (calling a tool, searching, running code), then feed the result back in before reasoning again.

Claude

Giving Loops Memory

For loops that run over many steps (research agents, multi-turn assistants), summarize prior steps into a short running memory instead of replaying the full history every time — this keeps context small and cheap..

Any model

Planner–Worker–Reviewer Teams

The most robust agent architecture is also the simplest: a Planner decomposes the goal into tasks, Workers execute one task each with narrow context, and a Reviewer checks results against acceptance criteria before anything ships.

Any model

Loop Budgets and Failure Handling

Production loops need budgets (max iterations, max cost, max time) and explicit failure paths: what happens when the Reviewer fails a result three times? Options: escalate to a human, fall back to a simpler method, or return a partial result flagged as unverified.

Any model

Project: Draft–Critique–Revise Content Machine

Deliverable: a working 3-role loop (writer, critic, editor) that produces a publishable piece of content on any topic you give it. Steps: 1.