Introduction
Prompt engineering is the key to unlocking accurate, useful, and safe outputs from generative AI systems like ChatGPT, Claude, or Gemini. Whether you’re building AI products, automating content generation, or conducting research, knowing how to prompt effectively can save hours and deliver higher-quality results.
In this guide, we’ll explore 5 essential prompting techniques for generative AI—each explained with real-world examples. These strategies help reduce hallucinations, improve formatting, and increase the AI’s relevance and consistency.
1. How to Write Effective System Prompts
✅ Tip: Define role, constraints, and context clearly and concisely.
A system prompt instructs the AI about who it is and how it should behave. It sets the groundwork for all future interactions.
Bad Prompt:Write a story. → Too vague
Good Prompt:
You are a helpful and knowledgeable medical assistant. Your goal is to provide accurate and easy-to-understand information about common health conditions, but you must always advise users to consult a doctor for diagnosis and treatment. Do not give direct medical advice.
- Role: Medical assistant
- Constraints: No diagnosis, general info only
- Context: Health-related queries
This ensures consistent, safe, and domain-specific responses.
2. How to Use Few-Shot Learning
✅ Tip: Provide 2–3 example input-output pairs before giving a new input.
Few-shot prompting helps the AI “learn by example” in real time, especially useful for custom formats or niche topics.
Use Case: Summarizing articles in bullet-point format
Prompt:
Summarize the following articles into 3-5 bullet points. Focus on key takeaways.
Example 1:
Input: 'The quick brown fox jumped over the lazy dog.'
Output:
- Fox jumps over dog.
- Action occurred in a sunny environment.
Example 2:
Input: 'Generative AI creates new content like text and images.'
Output:
- AI generates text and images.
- Uses machine learning models.
- Learns from existing data.
Now, summarize this article:
Input: '[Insert your text here]'
By modeling the desired format, you reduce ambiguity and improve accuracy.
3. How to Implement Chain of Thought (CoT)
✅ Tip: Add “Let’s solve step by step” for logical reasoning tasks.
CoT prompting helps the AI walk through problems like a human would—especially useful in math, logic, or coding.
Scenario: Apply multiple discounts to a product price.
Prompt:
A product costs $100. It has a 20% discount, and then an additional 10% off the discounted price. What is the final price? Let's solve step by step.
Expected Output:
1. Initial Price: $100
2. 20% off → $20 discount
3. New price: $80
4. 10% off → $8 discount
5. Final price: $72
Adding “Let’s solve step by step” often activates better logic sequences.
4. How to Control AI Output Format
✅ Tip: Use delimiters, markers, or define structured formats like JSON or XML.
This ensures consistent formatting for downstream use in apps, databases, or APIs.
Use Case: Extract product data from a paragraph
Prompt:
Extract the following information and present it as JSON:
Keys: product_name, price, availability. Use 'N/A' if missing.
Text: The "SuperWidget Pro" is available now for $49.99. Limited stock remaining!
Expected Output:
{
"product_name": "SuperWidget Pro",
"price": "$49.99",
"availability": "limited stock"
}
Such formatting helps integrate AI into pipelines and automated systems with ease.
5. How to Reduce Hallucination in Generative AI
✅ Tip: Ground responses in facts and clearly restrict AI’s scope.
Hallucination occurs when AI fabricates plausible-sounding but false info. This is dangerous in legal, medical, and financial domains.
Use Case: Respond strictly based on a provided document.
Prompt:
Answer based *only* on the following document. If the answer is not there, say "Information not found in the document."
---
Document: 'The company’s Q1 2023 revenue was $10M. Main product is Widget X. 50 employees.'
Questions:
1. What was the Q1 revenue?
2. What’s the main product?
3. When was the company founded?
Output:
1. $10M
2. Widget X
3. Information not found in the document
This significantly lowers the chance of AI “making things up.”
🔚 Conclusion
Generative AI can be incredibly powerful—but only when guided well. These 5 techniques empower you to:
- Set the right tone and boundaries (system prompts)
- Guide behavior through examples (few-shot learning)
- Improve logical accuracy (Chain of Thought)
- Get structured output (format control)
- Avoid misinformation (reduce hallucination)
💡 Next Step: Try combining these techniques in your prompts to tackle more complex tasks with confidence.
✅ Pro Tip:
Save reusable prompt templates in your workflow, especially for repeated tasks like summarization, classification, or data extraction.
