Skip to main content
Few-shot prompting is a powerful technique for guiding an LLM to produce a specific, structured output. It involves providing the model with several examples (the “few shots”) of the task you want it to perform. This in-context learning helps the model understand the pattern, format, and nuances of your request far more effectively than a simple instruction.

​
From Zero-Shot to Few-Shot

To understand few-shot prompting, it’s helpful to first understand its counterpart, zero-shot prompting.
  • Zero-Shot Prompting: This is what we’ve been doing in most of the previous examples. You give the model an instruction and expect it to perform the task without any prior examples. (e.g., “Summarize this article.”) This works well for simple, common tasks.
  • Few-Shot Prompting: When a task is more complex, novel, or requires a very specific output format, zero-shot prompting can fail. By providing 2-5 high-quality examples, you essentially “show, don’t just tell” the model what you want.

​
Why Few-Shot Prompting is a Game-Changer

Few-shot prompting is one of the most reliable ways to improve the accuracy and consistency of an LLM’s output.
  • Pattern Recognition: It forces the model to recognize the underlying pattern in your examples, making it much more likely to replicate that pattern in its own output.
  • Format Control: It’s the best way to get the model to produce output in a specific format, such as JSON, XML, or a custom-structured text format.
  • Task Specialization: You are essentially creating a temporary, specialized model for your exact task, without the need for expensive fine-tuning.

​
A Practical Case Study: Data Extraction

Imagine you have a block of unstructured text and you want to extract specific pieces of information into a structured format. Zero-Shot Prompt (Likely to Fail): 
Extract the name, company, and job title from the following text:

"John Doe, a Senior Software Engineer at Acme Corp, is leading the new AI initiative."
The model might return a sentence, or it might not format the data correctly. Few-Shot Prompt (Much More Reliable): 
Extract the name, company, and job title from the following texts.

Text: "Jane Smith is the CEO of Global Tech."
Output:
{
  "name": "Jane Smith",
  "company": "Global Tech",
  "title": "CEO"
}

Text: "The new project will be managed by Mark Johnson, a Project Manager at Innovate Inc."
Output:
{
  "name": "Mark Johnson",
  "company": "Innovate Inc",
  "title": "Project Manager"
}

Text: "John Doe, a Senior Software Engineer at Acme Corp, is leading the new AI initiative."
Output:
By providing two clear examples, you have given the model an unambiguous template to follow. It will now almost certainly return a perfectly formatted JSON object for the third example.

​
Best Practices for Few-Shot Prompting

  • Quality over Quantity: 2-3 high-quality, clear examples are better than 10 confusing ones.
  • Consistency is Key: Ensure the format and structure of your examples are identical. Any inconsistency can confuse the model.
  • Use Realistic Examples: The examples you provide should be representative of the real data the model will be working with.
  • Include Edge Cases: If you know there are tricky edge cases in your data, include an example of how to handle them in your prompt.
Few-shot prompting is a fundamental skill for anyone looking to move beyond simple tasks and unlock the full power of LLMs for complex, structured work.