Prompt Patterns: Roles, Constraints & Rubrics — A Complete Guide

The 7 Essential Prompt Patterns for Business

Prompt engineering is not about memorising magic phrases. It is about understanding a set of patterns that consistently produce better results. These 7 patterns work across all AI tools — ChatGPT, Claude, Copilot, Gemini — and all business contexts.

Pattern 1: Role Prompting

What it is: Assign the AI a specific expert persona before giving your request.

Why it works: AI produces more relevant, detailed outputs when it has a clear perspective to adopt. A "senior HR consultant" gives different advice than a generic AI.

Template:

You are a [specific role] with [years] of experience in [domain]. You specialise in [specialisation]. Your audience is [who you're writing for]. [Your actual request]

Business examples:

• "You are a CFO with 20 years of experience in Singapore financial services..."
• "You are a compliance officer specialising in PDPA and data protection..."
• "You are a management consultant who advises mid-size companies on AI adoption..."

When to use: Always. Role prompting should be your default starting point.

Pattern 2: Constraint-Based Prompting

What it is: Set explicit boundaries on format, length, tone, scope, and content.

Why it works: Without constraints, AI tends to produce generic, meandering outputs. Constraints force precision and relevance.

Template:

[Request]. Constraints:

• Format: [table/bullets/paragraphs/numbered list]
• Length: [word count or page count]
• Tone: [formal/conversational/technical/executive]
• Audience: [who will read this]
• Must include: [required elements]
• Must exclude: [elements to avoid]

Business examples:

• "Maximum 200 words, written at a Grade 8 reading level"
• "Output as a table with 5 columns: [specify columns]"
• "Use British English spelling, formal tone, no jargon"

When to use: When you need control over the output format.

Pattern 3: Chain-of-Thought

What it is: Instruct the AI to reason through a problem step by step before giving an answer.

Why it works: Forces the AI to show its reasoning, which produces more accurate and defensible conclusions. Also makes it easier to spot where the logic goes wrong.

Template:

[Question/Task]. Think through this step by step:

1. First, [initial analysis]
2. Then, [secondary consideration]
3. Next, [evaluation]
4. Finally, [conclusion/recommendation]

When to use: Complex analysis, financial calculations, strategic decisions, and any situation where the reasoning matters as much as the conclusion.

Pattern 4: Few-Shot Prompting

What it is: Provide 1-3 examples of the output quality and format you expect before asking for new content.

Why it works: Examples communicate quality standards more effectively than descriptions. The AI matches the pattern, tone, and detail level of your examples.

Template:

Here are examples of what I want:

Example 1: [input] → [desired output] Example 2: [input] → [desired output]

Now do the same for: [new input]

When to use: When consistency across multiple outputs matters (job descriptions, product descriptions, email templates, social media posts).

Pattern 5: Rubric-Based Prompting

What it is: Provide explicit evaluation criteria and ask the AI to assess against them.

Why it works: Produces structured, fair evaluations. Reduces subjectivity and ensures completeness.

Template:

Evaluate [subject] against this rubric:

Criterion	Weight	1 (Poor)	3 (Good)	5 (Excellent)
[criterion 1]	[%]	[description]	[description]	[description]
[criterion 2]	[%]	[description]	[description]	[description]

For each criterion, provide: score, evidence, and recommendation.

When to use: Vendor evaluations, performance reviews, proposal assessments, quality audits.

Pattern 6: Comparative Analysis

What it is: Ask the AI to compare options side-by-side against defined criteria.

Template:

Compare [Option A] vs [Option B] vs [Option C] on these dimensions:

1. [Dimension 1]
2. [Dimension 2]
3. [Dimension 3] For each dimension: explain the key differences, rate each option (1-5), and identify the clear winner. Conclude with an overall recommendation.

When to use: Technology selection, strategic alternatives, vendor comparison, policy options.

Pattern 7: Iterative Refinement

What it is: Start with a broad request, then refine through follow-up prompts.

Template — Round 1: Generate the first draft. Round 2: "Make it more concise — cut to 300 words" Round 3: "Add specific data points and examples for the Malaysia market" Round 4: "Change the tone from academic to conversational" Round 5: "Format as a table with action items"

When to use: Always. Rarely is the first output perfect. Plan for 2-4 rounds of refinement.

Combining Patterns

The most effective prompts combine multiple patterns:

Role + Constraint + Chain-of-Thought: You are a senior management consultant. Analyse whether Company X should enter the Indonesian market. Think step by step. Maximum 500 words. Output as: Executive Summary, Analysis, Recommendation.

Role + Few-Shot + Rubric: You are an HR director. Evaluate these 3 candidates using the scorecard below. Here is an example of how I want each evaluation structured: [example]. Now evaluate: [candidates].

Quick Reference Card

Related Reading

• Prompting Structured Outputs — Get consistent, formatted results from AI tools
• Prompting Evaluation and Testing — Systematic approaches to testing prompt effectiveness
• ChatGPT Output Evaluation — How to evaluate and improve the quality of AI outputs