The CO-STAR prompt framework explained, and how it compares to RCTF
What each part of the CO-STAR prompt framework (Context, Objective, Style, Tone, Audience, Response) means, an example, and when to use CO-STAR or RCTF.
By the ThinkWithPrompt editorial team at Synottic · Updated 11 October 2026
CO-STAR is a prompt structure that became popular in 2023. Its strength is that it separates style, tone and audience into their own parts, which makes it well suited to writing where voice matters as much as content.
The six parts
- C: Context
- The background the model needs: the situation, the product, what has happened so far.
- O: Objective
- The task, stated as a clear goal.
- S: Style
- The writing style to imitate, for example a specific kind of publication or an expert explaining to a beginner.
- T: Tone
- The attitude of the writing: formal, warm, urgent, reassuring, playful.
- A: Audience
- Who the output is for and what they already know.
- R: Response
- The format of the answer: length, structure, or a format such as a table or JSON.
An example
CO-STAR vs RCTF
| CO-STAR | RCTF | |
|---|---|---|
| Who the model is | Implied by Style | Explicit Role |
| Background | Context | Context |
| The job | Objective | Task |
| Voice | Separate Style and Tone | Part of Context or Format |
| Reader | Separate Audience | Part of Context |
| Output shape | Response | Format |
They cover nearly the same ground. CO-STAR makes voice and reader impossible to forget, which suits marketing, communications and content. RCTF’s explicit role tends to work better for analysis, planning and expert tasks, where the perspective the model reasons from matters most.
Which should you use?
- Writing for a particular reader where voice matters: CO-STAR.
- Analysis, planning, decisions or expert review: RCTF, or RCTFVV when accuracy is critical.
- Not sure: use either. What matters is that context, task, audience and format are all present, not the label.
The prompts in our library use RTF, RCTF and RCTFVV structures, and most already include the audience and tone as fill-in blanks. Read the RTF vs RCTF vs RCTFVV comparison for more.
Frequently asked questions
What does CO-STAR stand for?
CO-STAR stands for Context, Objective, Style, Tone, Audience and Response. Context is the background, Objective the task, Style and Tone how it should read, Audience who it is for, and Response the format of the answer.
Is CO-STAR better than RCTF?
Neither is better in general. CO-STAR is strongest for writing where voice and reader matter, such as marketing and communications; RCTF's explicit role suits analysis, planning and expert tasks. What matters most is that context, task, audience and format are all present.
Audience-focused prompts to try
- Write an automated email sequenceWelcome, nurture, onboarding, or launch sequences with timing, subject lines, and full emails.
- Write a campaign brief and channel planA campaign brief with objective, audience, message, channels, timeline, and measurement.
- Create a brand messaging framework and voice guideCore messages, proof points, and a voice and tone guide so everything sounds on-brand.
- Plan a product launch and go-to-marketLaunch messaging, audiences, channels, timeline, and success metrics.
- Write ad copy variations and a testing planAd copy for Meta, Google, LinkedIn, or TikTok with angles, variations, and what to test.
- Write a press release and media pitchA newsworthy press release plus a short pitch email for journalists.
More guides
- How to write a good AI prompt: a practical guide
- RTF vs RCTF vs RCTFVV: which prompt framework should you use?
- Few-shot vs chain-of-thought prompting: when to use each
- Prompt meaning: what is a prompt in AI? Parts and examples
- Prompt meaning in Hindi: प्रॉम्प्ट का मतलब, AI प्रॉम्प्ट क्या है और उदाहरण
- What is prompt engineering? A practical beginner’s guide
- What is a system prompt? How to write one, with examples
- Prompt templates: how to build reusable AI prompts, with examples
- How to prompt reasoning models: what changes with “thinking” AI
- Prompt chaining: how to break big AI tasks into steps