7 AI Prompt Techniques for Better Workflow Results

Getting consistent, high-quality outputs from AI models is critical when they’re integrated into your automated workflows. These prompt engineering techniques will help you achieve better results every time.

1. Use Role Prompting

Instead of: “Write a sales email for our new product.”

Try: “You are an expert sales copywriter specializing in B2B SaaS products. Write a sales email introducing our new team collaboration tool to a potential enterprise client.”

Why it works: Giving the AI a specific role creates context and helps it align its response with the expertise you want it to emulate.

2. Include Format Instructions

Instead of: “Generate some FAQs about our service.”

Try: “Generate 5 FAQs about our service. Format each as a JSON object with ‘question’ and ‘answer’ fields, where answers are between 50-75 words.”

Why it works: Specific format instructions ensure consistent outputs that can be easily processed by downstream automation.

3. Use Few-Shot Examples

Instead of: “Categorize this customer feedback.”

Try:

Categorize this customer feedback into one of these categories: Bug Report, Feature Request, Praise, or Question.

Examples:
Feedback: "The app crashes every time I try to upload an image." Category: Bug Report
Feedback: "Would love to see dark mode implemented." Category: Feature Request
Feedback: "Your support team was incredibly helpful!" Category: Praise

Feedback: "The new dashboard layout is confusing to navigate." Category:

Why it works: Providing examples helps the AI understand exactly what you want and improves consistency.

4. Chain of Thought Prompting

Instead of: “Is this a good customer for our premium tier?”

Try: “Analyze if this customer is a good fit for our premium tier. Consider their usage patterns, company size, current plan limitations, and potential ROI from premium features. Think step by step before making your final recommendation.”

Why it works: Asking the AI to think step by step leads to more thoughtful, reasoned responses.

5. Set Clear Constraints

Instead of: “Write a product description.”

Try: “Write a product description for our cloud storage solution with these constraints: maximum 100 words, focus on security features, include at least one analogy, and maintain a professional tone.”

Why it works: Clear constraints prevent the AI from going off-track and ensure outputs meet your specific needs.

6. Use System vs. User Messages

When using chat-based APIs like ChatGPT, separate your instructions (system) from your content (user):

System message: “You are a data analyst who specializes in translating complex data into business insights. Your responses should be clear, data-focused, and actionable.”

User message: “Here are our Q1 sales figures by region: [data]. What are the key insights and recommended actions?”

Why it works: System messages establish persistent context while user messages provide specific content to work with.

7. Include Output Verification Instructions

Instead of: “Generate a regex for validating email addresses.”

Try: “Generate a regex pattern for validating email addresses. Then verify your regex works by testing it against these examples: [email protected], invalid@, user@domain. Explain why each test case passes or fails.”

Why it works: Asking for self-verification helps catch errors before they enter your workflow.