Zero-Shot vs Few-Shot Prompting: When to Use Each Approach

Zero-Shot vs Few-Shot Prompting: When to Use Each Approach

When you send a prompt to a language model, you are making a choice about how much context to provide. The two most fundamental strategies are zero-shot prompting — giving the model a task with no examples — and few-shot prompting — providing one or more examples before the actual task. Knowing when to use each approach can dramatically improve your output quality.

What Is Zero-Shot Prompting?

Zero-shot prompting means asking the model to perform a task without any demonstrations. You rely entirely on the model's pre-trained knowledge and your instruction clarity.

Classify the sentiment of the following customer review as Positive, Negative, or Neutral.

Review: "The delivery was fast but the packaging was damaged."

Classify the sentiment of the following customer review as Positive, Negative, or Neutral.

Review: "The delivery was fast but the packaging was damaged."

Zero-shot works well when:

• The task is common and well-represented in training data (e.g., translation, summarisation, basic classification)
• You need fast iteration and don't have labelled examples ready
• The output format is simple and unambiguous

What Is Few-Shot Prompting?

Few-shot prompting includes one or more input-output pairs before your actual query. These examples act as in-context demonstrations that steer the model toward the exact format and reasoning style you want.

Classify the sentiment of each review.

Review: "Absolutely love this product, works perfectly!"
Sentiment: Positive

Review: "Arrived broken, very disappointed."
Sentiment: Negative

Review: "It's okay, nothing special."
Sentiment: Neutral

Review: "The delivery was fast but the packaging was damaged."
Sentiment:

Classify the sentiment of each review.

Review: "Absolutely love this product, works perfectly!"
Sentiment: Positive

Review: "Arrived broken, very disappointed."
Sentiment: Negative

Review: "It's okay, nothing special."
Sentiment: Neutral

Review: "The delivery was fast but the packaging was damaged."
Sentiment:

Few-shot works best when:

• You need a very specific output format the model doesn't default to
• The task involves domain-specific terminology or reasoning patterns
• Zero-shot outputs are inconsistent or off-format

Choosing Between the Two

One-Shot as a Middle Ground

If you have limited context budget, a single example (one-shot) often provides 80% of the benefit of three or more examples. Start with one-shot and add more examples only when you observe format drift or reasoning errors.

Practical Tips for Few-Shot Examples

Diversity matters. Include examples that cover edge cases and boundary conditions, not just the easy cases. If you are classifying sentiment, include at least one ambiguous example.

Order can matter. Some research suggests the last example has the most influence on the model's output. Place your most representative example closest to the actual query.

Keep examples consistent. If your examples use a colon separator between input and output, use it everywhere. Inconsistent formatting confuses the model and reduces reliability.

Using Few-Shot with PromptProcessor

PromptProcessor's batch processing mode is ideal for few-shot workflows. You can build a template with fixed examples and a single {{variable}} placeholder for the actual input, then run it against hundreds of rows in one session. The examples stay constant while only the target input changes — exactly how few-shot prompting is meant to work at scale.