ChatGPT: the Pragmatic Editor
Will rewrite your design spec for clarity before you've finished explaining it.
👋 This post is part of the AI Prompt Blueprint series.
In the main post, I shared a prompt template built using the six building blocks of effective prompt design, then asked four top AI models to provide feedback. Each one had strong (and different) opinions.
You can keep reading for ChatGPT’s feedback or jump to your favorite model’s feedback below:
ChatGPT approached this prompt like a professional editor reviewing a work document before it goes to production. It wasn’t trying to reinvent the format or question the premise. Instead, it focused on polishing the language, generalizing the use case, and tightening ambiguity so that the final result would be clearer, more robust, and easier to reuse.
While the other models we got feedback from spent time looking for gaps in the blueprint, ChatGPT used it’s tactical editing skills to make the copy sharper, the roles more specific, and the instructions more foolproof.
Tactical Edits, Not Reinventions
ChatGPT stepped in like a seasoned editor, scanning for subtle misalignments between what I said and what I meant, looking for ways to make everything clearer, more consistent, and easier to reuse.
Like the other models, ChatGPT quickly flagged two common prompt pitfalls: unclear roles and weak synthesis instructions. It suggested shifting the assistant’s title from “personal shopping assistant” to “product expert” to avoid persuasive framing, and added a line reminding the model to “synthesize relevant insights” rather than just echo reviews. Small changes, but they help the prompt do its job more reliably.
Then it flagged the <relevant_reviews> section of the answer. Was this for internal use or part of the visible output? ChatGPT surfaced the ambiguity and offered two clear paths: make it part of the answer or label it explicitly as a reasoning step. This kind of clarification is what helps prompts scale across different tools and use cases.
💡 ChatGPT was the only model to suggest adding a second example that showed the model how to respond when it didn’t have enough information.
Not just a fallback line of instruction, but an actual demo of when and how to use it. That detail reflects ChatGPT’s core strength of turning fuzzy instructions into teachable behavior.
None of this feedback was flashy. But that’s the point. ChatGPT behaves like a pragmatic editor. It’s focused on polish, generalization, and instructional precision. It won’t rewrite your idea, but it will make sure your prompt is clear, durable, and ready for production.
ChatGPT’s Strategic Priorities
Given ChatGPT’s “Pragmatic Editor” archetype, it’s no surprise this is the model I reach for when I just need to get something done. It’s friendly, practical, and doesn’t overcomplicate things. When I ask a question, it jumps straight in to help. While the other models helped me think about how to write a better prompt, ChatGPT was the only one that rewrote it for me in an easy to copy+paste section.
That reflects OpenAI’s broader philosophy: build practical AGI through broad access and rapid iteration.
Where the other models discuss blueprints, ChatGPT builds a working prototype. Its feedback is grounded in usability: Will this prompt work across teams? Can it be reused? Is it clear enough to hand off? That mindset mirrors OpenAI’s focus on real-world adoption from plugin support to workflow integrations to becoming the default AI for millions of users.
Takeaways for Builders
If you need a prompt that works reliably under pressure, ChatGPT is your go-to. The Pragmatic Editor isn’t here to philosophize. It’s here to ship. It focuses on clarity, structure, and making sure your prompt won’t break when reused, edited, or handed off.
Use this approach when:
You’re building a prompt template to use across multiple tools or workflows. ChatGPT’s suggestions help generalize without losing focus.
You need consistent output formatting, even when the inputs vary. Its edits tighten ambiguity and enforce structure.
You’re handing the prompt off to a teammate, stakeholder, or production system. It rewrites with clarity and error-proofing in mind.
When in doubt, write the prompt like someone else will have to use it. ChatGPT will make sure they can.
👋 I shared a prompt template built using the six building blocks of effective prompt design, then asked four top AI models to provide feedback. Each one had strong (and different) opinions.