Give AI Its Best Shot—Guide It with Examples.
Multi-Shot Prompting
When you ask AI a question, you might expect it to just know what you want. But AI doesn’t “understand” in the way humans do—it recognises patterns based on what you give it.
That’s where One-Shot and Multi-Shot Prompting come in.
🔹 Zero-Shot Prompting – You give no examples and just hope for the best. AI makes a best guess, but the results can be hit or miss.
🔹 One-Shot Prompting – You give one clear example. AI follows the pattern and improves its response.
🔹 Multi-Shot Prompting – You provide multiple examples, reinforcing consistency in style, format, and logic.
How to Use It in Practice:
✅ Give an example – Show AI exactly what you expect.
📝 Example: “Here’s how we typically introduce a new product: ‘[Example text]’ Now write an introduction for our latest feature using the same approach.”
✅ Maintain consistency – Use examples to keep tone and structure uniform.
📝 Example: “Summarise this report in the same style as the summary below.”
✅ Fine-tune complexity – AI adapts to the level of detail in your example.
📝 Example: “Here’s a simple breakdown of a marketing strategy. Now explain our new pricing model in the same way.”
If AI’s responses feel off-track, don’t just rephrase your request—show it the right path with Multi-Shot Prompting.
Get AI to Think Before It Speaks
Ever get an AI response that jumps to conclusions or misses important details? That’s because AI doesn’t always “think” in a structured way—unless you ask it to.
Enter: Chain of Thought Prompting.
Instead of asking for a quick answer, guide the model to reason step-by-step. This leads to clearer, more logical responses.
How It Works
Before vs. After:
🚫 Basic Prompt: “How do neural networks work?”
🤖 AI: “Neural networks process data through layers of interconnected nodes, learning patterns from input.”
✅ Chain of Thought Prompt: “Explain how neural networks work step by step, as if I’m 12.”
🤖 AI: “Imagine a classroom where a teacher shows pictures of cats and dogs. The students slowly learn to tell them apart by recognising patterns. A neural network works the same way—it starts with no knowledge, processes examples through layers, and gradually improves at making predictions.”
The result? More structure, more clarity and better understanding.
Where to Use It:
✅ For better reasoning in complex topics
📝 “Evaluate the pros and cons of outsourcing IT support. Walk through the decision-making process in steps.”
✅ For more reliable calculations and logic-based tasks
📝 “Solve this problem by breaking it down into logical steps.”
✅ For clearer business explanations
📝 “Explain how our pricing model works in a structured, step-by-step way.”
Asking AI to show the steps means better answers, fewer mistakes, and clearer thinking.