Lesson 48 lessons

Few-Shot Prompting Techniques

What is few-shot prompting?

Few-shot prompting means giving the AI 2–5 examples of what you want *before* your actual request. The AI learns the pattern from your examples and applies it.


It's like showing a new employee 3 sample reports before asking them to write one — they immediately understand the format, style, and level of detail you expect.

The structure of few-shot prompts

Format your few-shot prompt like this:


```

Here are examples of [what you want]:


Input: [example 1 input]

Output: [example 1 output]


Input: [example 2 input]

Output: [example 2 output]


Now do the same for:

Input: [your actual input]

Output:

```


The AI completes the pattern you've shown it.

When few-shot works best

Few-shot is most powerful when:

• You need a specific format that's hard to describe in words (e.g., tweet style, product descriptions)

• You want consistent tone across many outputs

• The task is classification or labeling (e.g., Sentiment: positive/negative)

• You have an existing style guide or brand voice to match


For simple, clear tasks, few-shot can be overkill — a good zero-shot prompt is faster.

Key Takeaways

  • Few-shot prompting gives the AI 2–5 examples to learn from before your real request.
  • Use the Input/Output format to show examples clearly.
  • Few-shot is best for specific formats, tone matching, and classification tasks.
  • Quality of examples matters more than quantity.

Write a few-shot product description prompt

Choose a product you know. Write 2 example product descriptions in your desired style, then ask the AI to write a third in the same style.

Input: Leather wallet Output: Slim, handcrafted from full-grain Italian leather. Fits 8 cards, a slim money clip, and your ID — nothing extra, nothing missing. Input: Ceramic travel mug Output: Double-walled ceramic that keeps coffee hot for 4 hours. No plastic taste, no metal tang. Just clean, pure flavor from the first sip. Now write in the same style for: Input: Wireless noise-canceling earbuds Output:
Context, Role, and Tone