CO-STAR Framework

The CO-STAR framework (Context, Objective, Style, Tone, Audience, Response) is a comprehensive prompt engineering methodology that treats prompt design as a full-stack challenge, ensuring every aspect of the AI's output is carefully specified.

Introduction

The CO-STAR Framework was developed by data scientist Sheila Teo and gained widespread recognition after winning Singapore's first GPT-4 Prompt Engineering competition. What makes CO-STAR distinctive is its explicit separation of Style and Tone, two elements most frameworks combine or ignore entirely.

While many prompt frameworks focus on what the AI should do, CO-STAR goes further by specifying how it should communicate. This makes it particularly powerful for:

• Content creation where voice and tone matter as much as accuracy
• Marketing copy that needs to resonate with specific audience segments
• Customer-facing communications requiring careful emotional calibration
• Multi-channel content where the same message needs different styles per platform
• Brand-aligned outputs that must match established voice guidelines

Origin & Background

CO-STAR emerged from the practical insight that most AI outputs fail not because of incorrect information, but because of mismatched communication style. A technically accurate product description written in an academic tone won't convert customers. A helpful customer service response written in a casual tone won't satisfy enterprise clients.

CO-STAR treats every prompt as a communication design challenge with six dimensions. Just as a graphic designer considers layout, typography, color, and whitespace, a prompt engineer using CO-STAR considers context, objective, style, tone, audience, and format.

• Style controls the structural approach: academic, journalistic, conversational, technical
• Tone controls the emotional quality: enthusiastic, formal, empathetic, urgent

By explicitly defining the audience before specifying the response format, CO-STAR ensures the AI calibrates complexity, vocabulary, and assumptions to match the reader's needs. A technical explanation for developers looks very different from one for executives, even when covering the same topic.

How CO-STAR Compares to Other Frameworks

• The output's communication style matters as much as its content
• You need to match a specific brand voice or editorial standard
• The audience has specific expectations for how information is presented
• You're creating content across multiple channels with different style requirements
• Emotional tone is a critical factor in the output's effectiveness

• For technical analysis where style is secondary (use RACE)
• For quick, simple requests (use APE)
• For step-by-step process work (use RASCEF)
• When the primary challenge is defining the AI's expertise, not its communication style

CO-STAR Framework Structure

1. Context

Context grounds the AI in your situation, reducing generic or irrelevant responses. Include the key facts, constraints, and circumstances that shape the task.

• "Our company is a Series B fintech startup with 200 employees, targeting small business owners in Southeast Asia. We just received regulatory approval in three new markets."
• "I'm a freelance UX designer pitching to a Fortune 500 healthcare company. They've had two failed redesign attempts in the past year."

• "I work at a company" (too vague)
• "We sell stuff online" (insufficient detail)

2. Objective

State what you want the AI to produce. Be specific about the deliverable and its purpose, as this focuses the AI on a clear outcome.

• "Write a product launch email that drives trial sign-ups from our existing free-tier users"
• "Create a competitive positioning document that helps our sales team handle objections about pricing"

• "Write something about our product" (undefined goal)
• "Help with marketing" (no specific deliverable)

3. Style

Style defines the structural and rhetorical approach. Reference known publications, writers, or formats to give the AI a clear stylistic target.

• "Write in the style of Harvard Business Review: analytical, evidence-based, with executive-friendly formatting"
• "Match the tone of Slack's product documentation: clear, concise, with helpful examples and a touch of personality"
• "Follow the narrative structure of a New York Times long-form feature"

4. Tone

Tone controls the emotional quality of the writing. It's independent from style; you can have a formal style with a warm tone, or a casual style with an urgent tone.

• "Confident and authoritative, but not condescending. Make the reader feel like they're learning from a trusted colleague."
• "Empathetic and supportive. Acknowledge the challenge before presenting the solution."
• "Urgent but not alarmist. Convey the importance of acting now without creating panic."

5. Audience

Audience definition ensures the AI calibrates vocabulary, complexity, assumptions, and examples to match who will actually read or use the output.

• "CFOs and financial controllers at mid-market manufacturing companies who are evaluating ERP replacements. They're analytical, risk-averse, and care most about ROI and implementation timelines."
• "Junior developers (1-3 years experience) who know JavaScript but are new to TypeScript. They learn best from practical examples."

• "Business people" (too broad)
• "Everyone" (impossible to tailor)

6. Response

Specify exactly how the output should be structured, including length, format, sections, and any special requirements.

• "Format as a LinkedIn carousel with 8 slides: hook slide, 5 content slides with one key insight each, summary slide, and CTA slide. Each slide should have a headline (max 8 words) and supporting text (max 25 words)."
• "Provide as a one-page executive brief with three sections: Situation (2 sentences), Analysis (3 bullet points), Recommendation (1 paragraph with clear next steps)."

Example Prompts Using CO-STAR

Example 1: Email Marketing Campaign

Example 2: Technical Blog Post

Best Use Cases for CO-STAR

Content Marketing

• Blog posts with specific editorial voice
• Social media campaigns across platforms
• Email sequences with progressive tone shifts
• Landing page copy with conversion focus

Brand Communications

• Press releases matching corporate voice
• Internal announcements with appropriate tone
• Customer communications during incidents
• Partner-facing documentation

Sales Enablement

• Outreach emails for different buyer personas
• Proposal narratives tailored to decision-makers
• Case study write-ups for specific industries
• Competitive battle cards with appropriate framing

Product Communications

• Release notes for different audiences (users vs developers)
• Onboarding sequences with progressive complexity
• Help documentation matching brand voice
• Error messages with helpful, human tone

Using Delimiters with CO-STAR

One of CO-STAR's best practices is using delimiters to separate each component clearly. This helps the AI parse your prompt more accurately:

You can also use XML tags, which LLMs process particularly well:

Common Mistakes to Avoid

1. Confusing Style with Tone

2. Vague Audience Definition

3. Skipping the Response Format

4. Overloading Context

Conclusion

The CO-STAR Framework is the premier choice for prompt engineering when communication quality matters as much as content accuracy. Its explicit separation of Style, Tone, and Audience gives you fine-grained control over how the AI communicates, not just what it says.

• Separate Style and Tone controls for nuanced voice management
• Audience-first design that ensures relevance and resonance
• Competition-proven methodology (Singapore GPT-4 competition winner)
• Reduces hallucinations by grounding outputs in specific context
• Natural fit for content marketing, brand communications, and multi-channel strategies

• Start with content tasks where tone and style directly impact quality
• Build a reference library of style and tone specifications you can reuse
• Test the same prompt with different Audience definitions to see how outputs change
• Use delimiters consistently to improve the AI's parsing accuracy