Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Prompt Decorator is a GitHub-based method that uses command-style modifiers like +++reasoning and +++Refine to improve AI responses from ChatGPT and Grok.
A growing online trend is drawing attention to “Prompt Decorator,” a GitHub-hosted method that claims to improve responses from AI systems like ChatGPT and Grok by adding structured modifiers to user prompts. The technique involves copying a text file from a public repository and attaching specific command-style instructions before submitting queries to an AI chatbot.
Key Facts
- The repository is hosted at GitHub.
- Users are instructed to copy a file named Prompt-decorator.
- Examples include +++reasoning and +++Refine(Iteration=3).
- The method is described as a way to improve answer quality.
According to social media posts circulating in early 2026, Prompt Decorator works by appending structured tags to prompts before sending them to AI systems. For example, “+++reasoning” is intended to instruct the model to perform internal reasoning before producing a final answer. Another modifier, “+++Refine(Iteration=3),” suggests the AI should revise its response up to three times before delivering the final output.
The approach does not require software installation. Instead, users manually include the copied decorator text in their prompts when interacting with AI platforms.
How to Use Prompt Decorator
To use Prompt Decorator, users first visit the public GitHub repository at github.com/smkalami/prompt-decorators. and locate the file titled Prompt-decorator. The full contents of that file are copied and then pasted into a conversation with an AI system such as ChatGPT or Grok.
After pasting the decorator text, users add structured modifiers before their main question. For example:
- +++reasoning — intended to prompt the AI to internally reason through the problem before generating a final answer.
- +++Refine(Iteration=3) — instructs the AI to revise its response up to three times before presenting the final output.
The modified prompt is then submitted as a normal query. The technique relies on structured prompt formatting rather than installing software or enabling hidden features. Output quality may vary depending on how clearly the instructions are written and how the AI interprets them.
