Prompt Engineering Course
Few-Shot Prompting
Few-shot prompting is an extension of one-shot prompting where you provide multiple examples before asking the model to perform a task.
Each example reinforces the desired behavior, allowing the model to better infer patterns, edge cases, and expected output structure.
This technique is one of the most powerful tools in prompt engineering because it directly reduces ambiguity without changing the model itself.
Why Few-Shot Prompting Is Powerful
Language models operate by pattern continuation.
When you provide multiple examples, you reduce uncertainty and guide the model toward a stable decision boundary.
Few-shot prompting is especially useful when:
- Tasks have subtle variations
- Labels or formats are strict
- Zero-shot results are inconsistent
How Few-Shot Differs from One-Shot
One-shot prompting shows the model a single behavior.
Few-shot prompting shows a range of behaviors.
This helps the model understand:
- What stays constant
- What can change
- How to generalize
Basic Few-Shot Prompt Structure
A well-structured few-shot prompt typically follows this layout:
- Clear instruction
- Multiple input–output examples
- New input to complete
Let’s walk through a concrete example.
Example: Intent Classification
Assume you are building a chatbot that classifies user intent.
First, define the task.
Classify the user intent into one of the following categories:
- Order_Status
- Product_Inquiry
- Refund_Request
Now provide multiple examples.
User: Where is my order?
Intent: Order_Status
User: Do you sell wireless headphones?
Intent: Product_Inquiry
User: I want my money back.
Intent: Refund_Request
Finally, provide the new input.
User: My package has not arrived yet.
Intent:
What the Model Learns from This
From multiple examples, the model learns:
- The allowed labels
- Language variations per intent
- Expected response format
This dramatically improves reliability compared to one-shot or zero-shot prompting.
How Many Examples Are Enough?
There is no fixed number.
In practice:
- 2–5 examples work well for simple tasks
- 5–10 examples help with nuanced tasks
- Too many examples can waste context
The goal is coverage, not volume.
Common Mistakes in Few-Shot Prompting
Many prompts fail because:
- Examples are inconsistent
- Labels change between examples
- Examples include noise or irrelevant details
Consistency across examples is more important than complexity.
How You Should Practice Few-Shot Prompting
To build skill:
- Start with a simple task
- Add examples one by one
- Test after each addition
- Observe stability improvements
This mirrors how prompts are built in production systems.
Practice
Few-shot prompting uses how many examples?
Few-shot prompting helps the model learn what?
What is the most important property across few-shot examples?
Quick Quiz
Few-shot prompting typically uses:
Few-shot prompting mainly helps models to:
Few-shot examples become part of the model’s:
Recap: Few-shot prompting stabilizes outputs by providing multiple consistent examples.
Next up: Role-based prompting and how assigning roles reshapes model behavior.