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.
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:
When to use: Always. Role prompting should be your default starting point.
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:
When to use: When you need control over the output format.
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:
- First, [initial analysis]
- Then, [secondary consideration]
- Next, [evaluation]
- Finally, [conclusion/recommendation]
When to use: Complex analysis, financial calculations, strategic decisions, and any situation where the reasoning matters as much as the conclusion.
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).
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.
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:
- [Dimension 1]
- [Dimension 2]
- [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.
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.
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].
| Pattern | When to Use | Key Phrase |
|---|---|---|
| Role | Always | "You are a [expert]..." |
| Constraint | Need format control | "Constraints: format, length, tone..." |
| Chain-of-thought | Complex reasoning | "Think step by step..." |
| Few-shot | Need consistency | "Here are examples..." |
| Rubric-based | Evaluations | "Evaluate against this rubric..." |
| Comparative | Decisions | "Compare X vs Y on..." |
| Iterative | Always | Multiple refinement rounds |
Prompt patterns are reusable techniques for structuring AI instructions that consistently produce better results. The 7 essential patterns are: role prompting, constraint-based, chain-of-thought, few-shot, rubric-based, comparative analysis, and iterative refinement. Each addresses a different type of business task.
Start with role prompting (always define who the AI should be) and constraint-based prompting (format, length, tone). Add chain-of-thought for complex analysis, few-shot for consistency across outputs, rubric-based for evaluations, and comparative for decisions. Most effective prompts combine 2-3 patterns.
Plan for 2-4 rounds of iterative refinement. The first output is rarely perfect. Round 2 adjusts scope and detail. Round 3 refines tone and format. Round 4 polishes the final output. As your prompts improve, you will need fewer refinement rounds.
